{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Chapter 7 – Ensemble Learning and Random Forests**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_This notebook contains all the sample code and solutions to the exercises in chapter 7._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To support both python 2 and python 3\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"ensembles\"\n",
    "\n",
    "def image_path(fig_id):\n",
    "    return os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True):\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(image_path(fig_id) + \".png\", format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Voting classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "heads_proba = 0.51\n",
    "coin_tosses = (np.random.rand(10000, 10) < heads_proba).astype(np.int32)\n",
    "cumulative_heads_ratio = np.cumsum(coin_tosses, axis=0) / np.arange(1, 10001).reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure law_of_large_numbers_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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7VMRVfZN+0ntSKBQKheJcoSw4p0GcJj+n3WHjq74juW9ADJXfHMOaX+BV//xid9qHn7KL6nRcccUVxMbG0rp1a6/yEKFZU05V1Afqxq1Oy5Ivu09W+JXZ7P4JQBuprHdbbcrr1BZ1hUKhUPxyUALnNAjg9qyGgELH6rD5lHi3eWHji3SsPAVAmLQhpcRaXEfu7M00HKs8p/OcNGkSV111FbfcejsAl0edIklXweyPt9BGV0EXfRF6j4xagXSbxeZg+hvf+5VPfd2/7OCpKtYcLOSpZYdcZXO/PujXTqFQKBSKC4VaovIgikoiLQ6qgjTd16fcjl6CFAIH3mrwu21bYcB413n2tGv8xutblEG1MZTykUMJevRR6tK0JZziN9NoM2/EOZu30WikT58+VFVVAaA3VzI+yFtE6XFw0O7OhSWlpKDKTKsozZcnu6Q24Nj7cip4aW0mV/VNcsXkueLFzQHbSim98mopFAqFQnGhUBYcDwzYWLHWLQye212PzmntsHs8tytFnSslA4AuuBas/k620cZQ/qoLInjgHS5x00jxm2l+7c8Eh9lM1cqVlC/61FUWFhbWZPu/jmhD+twJDOwQA0DHB5Zz8dPr6DB7GRmF1Ux4fhMAkcEGjs+b6BVB+dlvMxjxzHqklKw9VOg3doc4Tfg0+uMoFAqFQnGhURac0xBuB72HwDE6jw/r87wbOsr9OxtDubXDOO04yT+LeMOxSmxlZvRRQQh9YJ25b20OYdEmUvr7OwSXvPoapW++CUDMtdMB75QRvuzZvYuy0hKm9R/BjuPe8730P5tcxxvv1YRN29hQkhPCOFbsFnIbjhRz2/s7sceaEHYHotrKgqt70aFdJNNe2MJnu3LR6wT3XdaV2LAgFAqFQqG4UCgLjg/CZyeVwRkLxtOCs99w0qeTv7Awdb7iR69V8MwO8h7aQt3+koD1332Wyaq3DgSsK3v/fdexw8Oa9Pe//z1ge4vFQmZmJi3Cmta0/29cKjEewuSJKT296v/03g6kAOvAeCxDWtAwPokebaOYnJ5Nw7jWSAELd+TwyCrlj6NQKBSKC8sZCxwhRLgQoum1kF85vh4kep8lKlNKtF8fhwjwMeqbbxwr++iQX5lnkD2bxe5VV1lch/Sw1thK3AIpMjKSRx991HU+Zox3ss5g2fRup+6tIr3Oh6XE89bNA0ifOwEAe0IwjkTv+DtjDx5zHTeMbYVlYDxfxECbNzfxwppMOsxe5np9tjOHk6V1/HCstMk5KBQKhUJxLmj2U1gI8RfgfiDJeZ4LzJdSvvozze2C4CtwGqWLXQhAYmwdDrnebXZEhdHVdyCdMeD4hoQQbMWBt297cuKAWwRkp5XQqV8Ldq86QeuUaL58djcpcReTZN5EVvIUolZvpNXtN4HViq24GGNSEg/eeisVJhPxCQmsX7/eNVacyUZMqJFx3Vpy/+VdiQ4xkvLQCgDaxYX6zWN895YAOMINWPvFnX7SBh0yVkttbkuN5D+rMryq7/Xx0dn58Djiw00/+lkoFAqFQnGmNEvgCCEeBB4AFgDfOYtHAPOEEJFSynk/0/zOPz67gPTO3dVmYWOZcS8Dsxr8urzQKZbXnMe3jbufLuU5PBYUE3D4ig/vIWLyM0iLe9u2KTkKgFMPP0zl51/QNf0AOQfLXPWr304H0r3GOdrpavT2BnLbjCF3Fwzd/jDBmxd7tYmaciW6+fOZOXMmr7/+OgBfLV7Mnkfv82qX+eTl7D5RTtdEbwtOI3lmC5ZhLb3LRvfm/iO5fJivCbEQnaD+DFM7TH7pOzbeO4Ygg1opVSgUCsW5pblPlpnAHVLKuVLKtc7XHGCW8/WbwdeCY3A+s48bSsnXV7C09Du/Pocj3FYIISXr2/bDUeO/2whAWuu9xA2AIS4Ee1UVlZ9/AcCRf73C/g25gbp7caTzDNfxVv0lfvWVS5ZS/OKLlI8ew99GjQKgrq6OBQsWUFjonp9Rr2Nwsrd1ptZuJ3H9Xh7IyKX/9/4+NXohWNC1LQVj+lAwpg8ZI3r5tTFPSGLTvWPIeuoKlt41zK8+v9JM54dX8MDiNPIrm7ZqWR2SF08UUmn1jT2kUCgUCkVgmitwWgCBwt9uB1oGKP/V4utkrJf+TsZJSQdpkZjp01EPhhBKQhp9dCTSWk/Nyvuo/uoOdzubGVOy+2M3tAjBYbZx8vY/A+AQetbmezv3Npd1o1+hLjjeq6zkVc22dGrWna6ympoaNm8OHMumkVdOFgHwbp7bvycxSFt2OzrCP6O5USfIG92bRb07kdrg/rAGpWWg1wkqQ/U8Me0i/u/anix5cLRX34Xbc7j46XWU1DTQYLPjyzt5xTx1LJ8u3x0gcf1eKq021pdWBczirlAoFAoFNF/gZADXByi/Hjhy7qZz4fHzwXE5GbtrkjvtomPn3V7twsY/QcSkFzAagjABQZ3GIYwhSLNv+gNJ1ZL5GOJDiJnWGV2wgfr9JZgPaI7GdaE/TS8WJA5uVrsDBwLvzmrkuePeFqhQvY69w3pQMKYP4YbA29H1QjAqNoJNE7ytOYnr93LNviy+1Ft4oaKcCbsyOfT4ZUzu7Z1iYsC/1tDl4ZVUmK2sKqnE7hQwjx095dWuy3cHmJF2jL8c8tnNplAoFAqFk+YKnDnAo0KINUKIuc7XGuBh4LHmXkwIESuE+FIIUSuEOCGECCSaEELMEUJYhRA1Hq9kZ11nIcQSIUSxEKJMCLFKCNHFo+8tQgi7T9/RZzBHr/PGXVTSR/lY8XYirm6vJbN8MCmByglJbE5oOiaNJSuTlvf0JmxASywnqwGIuPIVTL1mUJTQz9WuVcWPZ/P2pS7EP2ZOI1c7l8AaqSgowFZW1kRrb55MbX4iTSEEBWP8k3Rurahxn+gFL83oy/45l7qKpF4gddD1+3T+uD+bpA37SFy/11XfNtg7ts7iwnIeO+oTk0ihUCgUCpopcKSUi4HBQAEwyfkqAAZJKb86g+u9AljQlrVuAF4TQvRoou0iKWW4x6txP3I0sBTo4hxnO7DEp+/3Pn03nMEcvWj0wXE4P6qkJM0fxeIjcJb03Y41uJjZ3bTyv/ULxXKarAWVS5diPujt2xKUPIaW/ToDEJ8USre9bzL0+4cYsHMeobXuZJ5xpe6M3wOrl5Oa+RlGiyaUiloOIPye+4l98BF3+z9rOaqMNhuPeMTJSbvzTjKHDsNaVOQ1j1q7e5no/9ok8G7PDsxo9SM7qAJwZHjTS20dN6WRVWcmIthI5pOXs+3BS2gY15qG8YGF1GXxkey4uLtf+Rs5xVz8w0E2llUzdc9RdlbWUmzxjyqtUCgUit8Xzd6+IqXcJaW8UUrZ3/m6UUq5p7n9nbFzpgKPSClrpJTfoQmVm85kwlLK7VLK/0opy6SUVuA/QBchxJk/gZuB24IjCA2tILnTLgCseFsTSoinsvVWLHq3qhl6aYTrOPb6rpiS3Y60+Q89TPbVU3E0eOeMCi7Ttmp3/fQu7byhgsiaHIbseILhW+5nlFxF/MW9ARh+TSrd75hM27wNDN862zXG0n3tWJ7eznUefe21hF+iOSFbcnK44w7NJ6i+WrOoZI13W1EA7j+iOTj/oUU0c1OTuDzBP/ZPc4gyGigY04cNg7qwvH+qX/2wbYe1g/IGFmcFdspu5Nku2vvZP6wHkxKiyPLwA8qut3Dtviy2VNQwaXcmF21Jb2oYhUKhUPxOaFLgCCFiPY9P92rmtToDNimlZ3CUfUBTFpzJziWodCHE6XZqjQQKpJSe0eP6CiFKhBAZQohHhBABt8MLIe4QQuwUQuz0LL9rey2fbNGiA+tcTsaC/gO+drXxXaLqQDalKV82OcnQXgnE3+a/08mW4+27bQqPA+nAaK1zlSV/vRSAIGsNPd+YT6/JfYjRCzr2jids+HDtvQARccGuPnWVFrodPkS3w4cIatOGmOlaMtDCx59ALNfi3tSFamJKNjSQm5bPqUzNX+jzQi2Vwx1tAy93SYcDGSD3licOq53c2ZvJnb2ZuI8z6RsawpxOrdngtFA18n52IXeuOMDcCu/0ET3L7bwRk8Cazh0pGNOHuCDtT5gQZOTtnh0JM+jJH92bzqHBBEItXfljd0gmvbTZFXjREWBb/2c7c+gwexmV9drfV0pJWa0lYFuFQqH4JXO6ODjFQohWUsoioAQIdIcTzvKmHU7chANVPmWVQESAtp8CbwKFaEtjXwghKqSUC70uLkQbtGUvz/wEm4CewAk08bQIsAFP+15ESvmm8zqEdOnsen+DTllZHuHgbowuC47L29iJxceC42hCK0rA1KGDNl+9npgbb6T8ww9d9Q3pX6CLboshXnMjCjGEEW+0ITw+bmPbjkRc9ab7fa8/ycgIAxFRJoReR8cvF6OPjUMetbH+g8MB5xE6aBAA9fv2YS3Ww8hU9vXtQ9fjBRgSurDk1UPogRtaXw8jVgLQN9I/8B9AzqxZ1G50569K/uZrTCkpXm0KnloEtNXeY1YleQ9v4cYpnah4dRcrgwSvdDbxdZKR+4/nQxu3WFyzroaP2xupy6rknu3astwXsy6mf3t/HS2EYNPgrmTWmtldVcel8ZEszC/j8axTvJFTzBs5xSzvn0q/yN9s4O0zYsHqIxzIc/8TTH5wOd/8dTgrDuRz8FQVNodkc6a2a6733NVM6NGSVeluy9q9E7rwp2Ed2Hm8nMHJsZiacDY/r1jNsOYxGPMgBEdd6NkoFIpfEKcTOGOBMo/jn/oTrgbwjSQXCVT7NpRSejqnbBVCvABMA1wCRwiRAKwGXvUUPh6+OgD7hRCPA/cSQOA0ha3hIAVRWmziRh+cep+3X4X3zbQBLRZOL7mHNNHXVV4REUlLnVv8JD78kJfAQdqxFaS5BA7AsHADXPUm9tKj1H3/AqfmfO+qsxbX0XBMW9ay11kxRJkI7tYNgAgfh+FXZq4D4M7XxqAL0VIs6OO7EDJoJrAWgENT/49BdSlMFJAVriN16EpX/xMnTtC+fXscdXUc6dcfgI5LvvISNwDHJk0m9bvNGOLjMR88SPbVUwlKvQxTj7Ze7SqWZAEQb5E8kG7m6yRvK9iN2RZadY3jzv0lQAhbsFGMZOpr3zOhR0seuLwboxdscLW/e2wKfxvfmdSwYFLDNEvOzLYJPJ7l3nV1xS5tO3/+6N5+DuS/Rax2B0a9DrPVzotrM7msZyJXvrzFq80Ng9vx0TZtB9qkl/zjOjXiKW4A/r3qCP9e5b1pcs8j44kONSKEIKOwGoeULNx2kkEd45jYq9U5elceWM3a/3UGyN4IH16tnW97HR6r0AJ1SukXsPO02Cyg02svhULxm6FJgSOl3OhxvOEcXCsDMAghUqWUjUFkeuMboreJ6eCxg1sIEYMmbpZKKZ88k77NwWEvcd0gGy04hQ4bXYFaQsmlHc8IzYl3ovyKZeIql0+Ow+dSj992N4uSvMVQ0vP/Ie///c11LgIk6wTQx6UQc/Mz2Dx0S+Gzu9zzrLZAlDvIYNtusVz1976seusA9dXuJaSs3cWk9G9B6IABWIo0UZFiT+SovoA0xwliwlOZMSzc69pTd63n3Y2V/Onaa4nJyXGVZ0+5KuBc63buIvKyCZQv/AQAY/IYHPUV2PL3EJQ8xq99kISdq6oZMEEz4B0Z1oOwPnYMMcHkztZi9HxJBHdSSxp2VqUX+j1wX1x3lKLqBuZNdW9L1wnBkeE96fKd9zb4Vhv28Vin1sxq1/Qus18jWcU1vLAmk5svbs+01zUhfN3AtnyyQ/ubvbohy6v92zcPYFz3lvzj0i70e+Jbv/Gev7YPV/VNYsnePO75ZC8vXNeHSb1a893REv74zna/9n0DjAGwe/sP7FjsH6xy+vTpJCYmEhvrtsjtPF7GtwcLuWtsCqU1FtrHhbrFqJTw+a2QvthvLD/meviKdZ0E1310+vY2C/wrwX1+916I7fjj11EoFL8KmpuqwQ40Lld5lscBRVLKH/3pI6WsFUIsBh4XQtwO9AGmAEMDXG8K2lJTBTAQuBt40FkXCawCtkgpZwfoezmwW0pZKIToCjwCfNac9+maK5LG/JmNK1NVQos+/ByzOeyx8asS7abaaMGx4J1baX/XHkSN9t4yba/wdixGHzhvFYDMk6FHAAAgAElEQVStLKTJOnuNvx9MUucY/vTMcF6d5c4/VZJbTUr/FrT/8ANyH1wLDhhm7cJRvbYENHOAvx9LfI02x3cXLWLGyh+IuOpNajc8iaPiBAUtBuIYdhljHp7CqQcfonrVKszpB4gYP46Kzz7D1OcmdCFaqoqGtIU0pC1E36I7QZ3G0f7tmSCgdsdJKr48yZJ3V1CfuYzy5A5EfaRZtqKvTqFi8VEAXiWMsVRhAW7FRBWSz3EnDP1kR46XwAHNuTl3VG8y68yM2eG2OMzNOvWLETgH8ioJNupIaeG/Qiul5LNdudznzN31wwOXkBjl/zdyOCSXPKv9Dlm6z221ahQ3niy9axjdWkVi1Gtf7NiwII7Pm8jhgipSEsIx6L2XWKf0SWJKH/eOtlGdE8h66gqyS2pJaRFOSU0DA/61xqvPLcGBYoF68+mnnwLQbfgV3L+m2KvujU1u4+uUPq35+/jOtN89//TipkUPuHUlzPO2FnL4G1jyF5jyirus6BDogyA2Gd4cBfk+YRhedP47Hfb/YOzDsOs9aDcEEv0DWyoUil8+zU222ZQFxAQ0nZ7anzuBd4AioBSYJaVMF0KMAFZIKRvNCNc525nQUlvOl1K+76z7A5ro6SGEuMVj7O5SypPAJcB7QohwNB+eD4GnzmCOgGRk5wTSfyjhQJCmcKROEzjZdPJqGYy2M+o4ycA6GjCRWtNApjOJpD3Awp4+xp2nqjjuIozxvWnr3+xHKX0vnYixbYm6tAMA1oJapM2BPsrEtdeksGzNSWrKLaSvPMGJb09y2eSO4NAsTRnl2+id0J71kWVUmry/BrdvXur1Bw8b/RAAn0y4GCmGkFAwEsphbEgoSc//h8PdulP61tuUvvU26E0EdRgBgCk1mjaHDyEtFg736k190UHyd45l04LV9Dj4DsEJXYgsPECktFO/qxRHfT26kBDCB7UiuFM0Bf/WfL/X+axsLpg3HoDLX9jMofwq7A6JXuf9FTXoBN3CQygY04d91XVM2Kn5tpdbbcQYm5/p/eegpsHW5NJQ77bR7MvxDg45ZsEGDj1xGZX1Vgb+aw0Wu4NurSI5lO/t0hYapOfS7i35aq8mdh6d1J28inoentityeW5pvKPBUKvE6S00P6JxoebOD5vIuU19ZSUVfLRO697tZVAdsuBmGURHQpPccSWQF+jW4Qd+m450aIHFTKwn9eSvXk8emgyCG0F+0+We5mhX8clYdmsZCgTzd8g78tGhDotQXMqoeoUfDkTUsbBt4/Ang+1148x63v49lE46rRGbXlee/ly81JIHvXj4ykUil8E4nTh7oUQjc67/wbmovnRNKJHS7jZVkrZ17fvr42QLp3lN6/Z6bL6PXaXrsE2eRrF6zI5ERrHO+OjuGbXQa7q9wg38wl24ba4zJP/j9lCuxmOkd+yXoxnZGkVm+LcDw7foHdSSmo3b6ZoxXpWlI/AAHQL0ZFsOjsfgDbzNEHRuLTTSMSYtmz8Jpt+Yf4P9EXZ8wkP78BjN97uKhtRZKPboW/83KVbOCIZYu3MUpMmOGJK+mGwhTP8qmSOrc0hv6qBYVtnE2StxdRjKkGpEwBIenIYwmkZOH7jjdTv3MW60e5f1P13/Zu4BS+xeqE7IvGYm7rSvmccYVEmqjfmUrki22/uLe7uS1DrcP66cA9f7zvFsI6xDO2cwFubj/Hg5d2YPlCTiw6zDRGkR+gE35VXM21vFu/27MDo2EhC9M2LkPD08kN8vO0kNwxpz+zL3TnjT1XU0zIy2E9YNeJwSKwOB0adjlEL1pNTpglhadJhNBmwVZ3+d4HUgfBIWabXCexN7GTK+NflXglL6yw2BIKQoOZ9n4qKioiOjiYoyNtx3m6343A4MBr9LYy1tbX8+9//dp2nt+rA5s59CBGCep97ykOxUfyxSxtuensb+3IrGWnMIllfRo0phDqjiYmxFlpUfEXH+lziIhO4O/Y1Xsoc7+p/o+UBvnO4rSiOMAPC5kA0uD+gp/5wEVf3S8Jk0Glirq4Mnml6ucmGnpN3H6JdVDwGvfNzqi6EpXdB5uqmP6zxj8Owe7RjKaG+HEKb2Ei6/3P44jbt+JJHIWs9dJ0IK2fD3Xsgqh18PB2G3Amp45q+5s9JkRZBnYSuYDODsWmLsUJxLhFC7JJSDvhZr/EjAqfx6dIezZLimSjIAhwHHpVSbvu5Jni+8BQ4u0q+JeS6beh1R1n/7Tu8fWkUVx7czLXdnucG4Y4G3OdIOv/oPIebhPcK2MTCUuZc0YWBu7QdKYGi+gIUn6zm06fcZv1+oXraBmkPKlNylMuZGLQ4OmUfazukjImhWAvc28gTZvai4utjWPM89WfTrMh9myprKVsHTGTLgIsB6JF3jBkn9pFr1R7Yt5hH833dTo7EBh4zoWAkw8L1xDsfrId2fkK7qiOEjdUCW4ePakPIqDaYQo1Ul5mxZmaw8N2SgGMF4vo5WsqJozsK6RxuoHrVCa/6xHsHkOuw8/CzW5iHZgUYThUtESy9ewQxDih6WYuCnPTUcGodDlI278eXD3slMy4usBXjVEU9Q+etc50nx4dxrKTWq82kXq34+kA+MsTAHcM6sCdGz4PJrZj69AbssSasA+PRnarDeLACGarHMjRwKg790SpGpsazdc1xHFFBWAdoOcX0gGF1HsLjn+mQ5Fh+OFbG5T0Tee3G/n5j2Ww2MjMzMRgMpKZ6xx+y2+2899579OjRg8GDBzN37lyv+muvvZYOHTpgNpt54YUXXOWXXnop1dXVJCcnExcXx4svvuiqe3foFTQYvcWRL/nJZkT7IUgpmZOZyxt5pQHbZW2eQKkjmnYU8Hy7G3n3+Cj+MKgHD1zWlaHz15M3NN6vj3F3KfbWoRgPlCOcJtOZozoxvU05YWvuJyflBrpENhDRbzo1IQk8l53Pq7nu7+KImHBGxEQwNjaCnhGh2MuOk5W9C123icRkryNO1oOtAb5yRquI6QjSDhXnMFVI9ylw9Vtg8FjiLjigLV+/dQmExUN5NkS3hzt/0Mp9l7Zzd0ILZzDMIB/LmMMBR9fAx9doefPiOkFJBn5c+RJ0uUK7nkLxM3LBBY7HRNYDV0spy3+08a8Ub4GzmvDrPwZg6/dP8crQLkzK2MCM1Je8BE7qyWzmtP2nVxnAtNJibmr7H6bUaStjTQmcFa/v59hetx9CK6NgUJgBa1wwHe8dSPE7B2jIKCfulh6EdI2l/mApunAjhrgQ6tNLXH4qvrzfIYj0KB3P7DP71W0s/IK9YXWk976V3SmaX0fvnCMMPnYYnXOnWKIjmkmW/hSLKpaYAvtVxBeM4Kroph9qSyo0/6Bxf+rO2vcO4vk1a9zoEgiJpCRRs0QlFIx09wFmvTSavEe2BO7oQ/jIJGo2uWPhhF3cim6RTQvAJ1KS+HPbBKSUNNgcBBv1PLf6CC+uC/wZu+cLDRP8oy8btxdjHZTg3+EsMa3Pp1fnOIw9YrmxdRyTEqLQC4FdSoJ0Ok7k5VFbXw8Wi8vPpZG4uDhKS0uZOXMmx48fZ+XKlU1c5cxpffPtPHrCLRZ626u4as9aqnNHU2vSsXxAKDkJTfuYnQlt7BZy9acRUlJiWq/5lTWMbImotSGjTi+8ApEaaiKzrsF1PrtjIn9um8An6bt4qDSIpXv+wqCq0+dyczH6QdAbYPN/wBltnOTRcGzDGc/rjBF6TYj9FJJHa3PtdzNc8SwYzvzzVCgCcT4ETrOcEaSU/ttgfsNIjy3hIVEngS5+uagAMtt1JOYtPa1uzyNfuB9yq+MjmW4p9u/gQ1KXGC+BU2iVnGhw0O+P2q+whFu9Ux2EdHcHaw4f1KpJgfNSF6fDc5qZIAlVllJqbVVEB8Wz4NKx5Ce282rf40SmS9wAjLFo102QbsuGzh5EC0c05YZyGoSVksTNvCMFf2oYg0DgQFKgq6CFI5Ic97OBNe96p6MAmPnyaDJ3FpGfVUn6pjyGX5NKxz7xfPDQ91RHHXK1qwvLwWqsxmasJrZ4ILU1ViJGt6V6g78TrQ07Ww0ZxMsIkhyxyP0lXn5Etd/n83y8ngWdgig6WIZoHUJDB7eD7yNH8wirs/Hgu1oS1Qcu7+oSN9lPX0HHB5Z7v4dRnSivtfCBzdui08jpxM3mAV0YsVNzfr4uMZZPCn48H1jDmFbsAKioYWtFDXc20S65OI/xeDvNlZZq1pLXX389YJ9//OMffPjhh5SWlGCzez8QRyaHsOlYvVdZcXgUX/R33hKc4uaubyqIqdWWjByMIgxJWIOdGzZWM2+a/xLOpO01pJ6yUhStp/iKSBLXrOB/Qy8/3UfgEjd3bPwKU0QkYUYDKy++lL01TiEvBA1j3VvTTyduphY5eOkPvfnL7iy+rK/zqvMUNwDzsguYl10Azp2SV/Z9hYHVmXzWewwmywFsGemUH70IU1ghkQMljqRxFL2VgcPioPXwIViL6jAO+RvC6LEsaq0Hu0WL3VOWDe9eAdXeSWVdxKVCVBKM+Ae8P/m0n5EXgcTNuDngsMGpvZqlpttkMEWAw645cy/+s3f7RiG2+3/a685tEN8ZdM1b4lUoLiTN9rYUQnRGi0XTDryj3Ekpbz3H87rAuB/2Ood2s5NN+FqE7NZjKHWAh0W3SpqQ0hGwvddVfMwYDqCqaywhLZoXmK7FX/tS9JJ3toydMW6/i9sHh/K/H+pYkfe2Nr7QkT/xca/2N33xKvG11dS3d8fhWV1aRrSoYkxsF24wj2BFzlvowm/BLoIYEFfIFuMR53iSbGMxydYWvBPsXspJqBhJU1z78EB0eh1dBifSZXAio6/vgpSSf/3rX3QblULxEbc1oDbC7X9TE3mUo7s60+eyDgR3j6VyRTZ12eUY0BN3c3feWf4xxTWluIzu9TCZYWSiZ7gz6vTwEjvDS+qBEDgC806U8vnwWHCm1/j7qUKCJSTZdbyy7AitHIKUbnGUWe2Yfaw0ta3D6VFkw2HT/s4vd23HXYdP0u5wFSeTw8Dp/7J0Yw1Fugr2RhvoVR/JJRNTWbjoI9ZefDE9emi78Z7vpgnOYouVi7akc3H+MZ4d0ocOrduxPquIG3xyhZ2OYwlJvDEqiW9XFyCRZOrz2W/wXkq5+uqryc3NZfv27cyePZvg4GBmzZoFc6KwOG8JQdgA2Ld/EgnVtzEx+kmWVTzE9lQTq/p5fz/H7a1ziZtGkvskcPlMzW+m0+IMvszShHyDQTDwqBmj89kbXmgj+d0yYDB//zqNiqgiQmq6sWSIkUsPbkcKQZDNyvKLLiYntiXX7liDDrBWV1EBDFn2CW/c8ieiWifRdYvbqtIvIoTd1fW0CjKycXBXrtmZwai0KibmWomzaP/uTu3ZykPAQ84+EhjoDFuweU01IXb4tK2RZ7q7d7B1rLGTHa5nR0QqHY7l0r8snNd39EIAFhIpygaDI51ik6C1WZL3sLfFMahjFLHXdqHs4yOuRLsAUZevJjy1FMeBJciUCRgMApL6Ya+X1O4sJKhtBJVfH0PfcSuRo1qhiwyl4VgFtvxyIrrVUnUgBOkIouF4JcaWocRa7kM22CgvnUp0u+3ox9+NTOjh8onzQ2+AXtMpSZ7CnR/u5tF+Zrrmfoq+Og9x8V2w6EawN8Cr2tIxl82D3jPgh1eh7SBIHqPFEbLUwsLrILsxVpaA2SfAEKxZlI4sh+Ij0P8WHOZKHJY6DG+OgKBwmPpfqDiBTP+SmsoySq9exPzNZaTlVtKtVQRDO8WTWVTDE1N6UNNg49uDhXSMD6NFRDDt4kLJLa/j2dUZDOwQy7huLUiIMP0uYl8pmqa5S1QTgS+APUB/YAfQCW2X02Yp5ZU/5yTPB41LVJ2/fZudxWuIvF6L53K87CIeipvDZcc2cFPHl/yWo9bPmsGkJ96gNt5t7egnt/MP5vMuf2YbF7OpezmtEv3jxyx5fg+5h8sJiwqitlJzOjWFGrj9uaYFgi+NjsU5oYL0SD0P93Y7CXY9eYzpqz6l3l6DPqgXn1w2juOt3b+m7+2QCLNvRyIwJ3XEFhmLrq6GsBOH0QsD0zr8g6NVe9hVupqL40/QMSqE2NuXcOStvSw2ud2uZgyZwcIf3EGmdbZgHnnifq+t6gCDJndk4ER/x89FixZx6JDbcmO0WLAG+f/6ji8YQcpVVpKTk/n444+b9fksrO+DAyNzRQgjCbxUMpwql4C59dtKksrs7EgxsbJ/84Tm+BwLTx/UhHB5izKirxrEwBOF/O2wmfEnS1zO2YG4/vrr6dy5M/X19cyfP9+rbqS1Gzm6Uo4aitnYuS+98rKIr6mkyhRKeVgELarK2dOuM2lttSjSj+wu5Il+bh+fNeuqibZC9BWJhKbUIltehE46ENV5WCqjKHr9ANGGl9D3vhTdybUYqr6j0HEXUm9BmkcD8H2Njd1Ret4d5+2n1KHQyqAMM0a7JLnQxh87PAR/eAUR34mwOO+4T1JKMrYV0KlfC/T2GkTxYUjqj92h4/W/bnC1i4wPZsajgynLr+XD51ZSFXOQCFtbDOUtMYeUEFrTBh06RHw5JeIIDr23o3ZudDyhlga6Z/emQV+Pzh6E0WDixpu6NGntbCQNG59joaNFz83GIPQ+D0Y7Wl4bAVQYYdxY7+39156wsKi9/3d26+pqgs4wRGpWg50ss4NOcUF0sp37FBl1g1qQPDkFfaNDtpOPt53kwS/9/dQAXprRl0kFryC+f/mczCGdVD5jkl/5CLYxkm3ocKBHUioj+LftWr6wj8SKAZAkiBp6G/KxoGePLQk9kkoZjGxis+8L1/XxCnnwi0RKLXBlm0FgroRDX0PJEchap1nwMldp7TqOhHFzoXXfMwtmeYZYcqtdPoy+RE5oT8SotiD4SQLyl+SDswv4XEr5tBCiGi1A3yngA7TM3c/9nJM8HzQKnPCCAeSt7UTUDYsAKKIFfxOvcXPVe0yI+NpL4Ny69FNuWvElk+a8Tm1L9039r/JZhrCVj7mJ1VzOe1zPkMGrCQtzbzGvq7Lw7n3aVuG/vD6WvWtOsuXzo0y7fwAtOwZ2et1cVs01+7LYN7QH4QYdSwormLC1hPpdRYwfFUa5KfCvs3++/jBBw3rw1EUzvMpf7NaO6VU7eGXOc5jtRqTTOabxK2sKC+OGp/7DO/doyTmnt0ujbVglFdabKbX/gQ+CN9EUf/nLXwgzRZFzsJQuQwJHtHU4HJjNZp555hmv8gm1teRlHeNQ927cERPLa9W+GT6aJqq0F5Vxaa7zYF0MIYWd2KfXU9stkjsSa9m8byuJjmg62lvQw96WyCs6cnVuHgdaaUt7sdV2yiL8dyBdtquW7sYgnuvlLZR2rKr2u7XasPNe8IZmzfm2227jgw8+wGL58YgLbeyxmIWVVHsreti13WIt/tqXur1F1GzO43CEjhuHNi3MrsnL4OH0aqzSOydYsUlw+Wh3sMdwq6TGGPjmNSoni4VZt1Fpb03wjNcJ6dG0ILdXWyh5Px1h0BE5th3BnbUQCZacaoreSEMXpEN/ZSfMK4+jr2gg6YlhCKNO22l4rJKgYIOXldLskKTV2zEJQa7VQfvYfH4wZjZ1efRSj13YSXREM9HSjzyLJPqyDmz8Ohub9fRW1nCdZtzTxQZz3dyLOZVZwZL/uOdSGq7j1YnNS0SburuC0ZV2puhMJJq1e+4z5nq6CD0d7XraRen5NtHIqRAdYTbJ7ccs1GLGgB6TU5hX2CTRBu+/iRUwAtVIbKKOUtFAO0ckRmf2HHEGMU6XYmEFVk4EC6rMNld5MFauMh0gWLjLJsq19BXpFBFHAQkky5NsYCj9yGKnvT05w+fy8oZjPGr4Hzca1lBFOEFYOShT+FpcGujyP5lVls60b9+BH7LLCMVCPUakx55QPXa+urYFZKyiTcUOLAUHaelMYfj2RQu54Yox2D79kspDbdGHNmCv0+4H4cNaE3VFMuh+2gMdoHZHAeVfaN9XQ4SV6BF6TGuvdGmVCusd1Njd9gKDOI5DRuPA/T3TUUqQLgObbEOE/gtC9WsQqeOxTfwAW5kZER9CXVYlDeu1pXzh3J0b1DqcoHYR2Mob0AXpCGoXibFlKLpQI0eLqvnnZ2lMKrBymbX5u3lNnaKw5NYgG5rp66UXWuwUAW3njfzFCJwaoJeU8pgQogwYKaU8IIS4CFgmpWz3I0P84mkUOACVH00n6gbNUbOUOO4Wb3K7fJUxrPUSOP957nH6ZB5i8qOvUdPK/QWcJV/gyljJ22WJfCGu4wN5Df16v0Nc3AhXm1dmrsOqh+oQHY8sGI2UkpryBiJiAyePxG7j5h+2s9oSyn87hvOdJZh380q4NjGWp5OiSN7lv526kZTSHI7GuSPt9K06yJ7I7nzUPYlLTnxF3dIHeC1zCACpg4eSuW2rq+3fP/ma565zr/uPT8zk2wJtZ05S50gO690PyvDKVGqivB82d999N7GxsdTU1LBgwQJuuOEGMjIyKC8v5+jRwL+qH3roIWRhIVnjtK3CZTExfDvh9DfF0aNHYz/ZgsNbNSfTmohj1Id5R9KNKruIyljvX6ht7XEkOeLQ2U3cPME7xpEnN23YQuLhFegM7WiVOp6jrVrzcmcT156wcO9hzXrTYK/HpNcsaG8Hr/XqH3Y0DYTgkjZ/IlKGkkMhG4OP+F1n1tRbsX2Rx1uscpUNsaZSJmpwCMlYUx8cdVbQ6Wj9yGB0JgPYLEgEtbtLsObWED6lE203pfmN7YlOSlqaJXV6mJBv49MA1gdftiS1IaZjNLHNjCNkKzNT8Iy3k3rYxa2o/T6/Wf2bi0Qip7Vh7bfrONHgHz3ZhUNHbMkA9I4m/o056Ta0FYe2Nn+Onw4L50ibIO5YWUlctR29A6SAeVNjsOsDPxBv2niYNsXhlEZVs75XOJktm46ElVBVTpvyIhJqKumZbSS0ti3C48EtcWAzVlMRt8+vb700EiKsnLLHsdqaTByCJT7p/xw4EM7/AA4kl/DDKf+xzgSjCKZWZ8Not1EbFIwUArMxiFpTCNs6dmdUxh5K9d3YkW/lyt6tuWtsCte9tpnL5E6CxI8v73cng2Aa2M1FNLYO9PMutfNFRKbFkKTbQE/DMoot85xtq7GTQLThdewymmr7dWf1PsMGJyJrK9FlfE4YS6gLno5Rn0NI3ddIjBRZFmAUWUjCMTt+1mf5Oecp6lmPlUYPPIG2q3MmJq7zCWh7NrSd//MLnOb64FQDjXeFfCAFOODsH9NUp18rnk7GOufOePvp8on63MP6sx27vQcmNOdHMyYk/gr3i6HhZLYO4kEp0QvRtLgB2DgfSqMhfhi3ZdfQGJJoUUEZ99TkAE3/avcUNwDTClezJ7I7Ke8Oh9AwQg1Wbnv+DfIyDtNj1CVs/vg9ti/5XHtrPr9YGsUNQG5GFcMHn+KArjtjx46lV69ebN26ldWr3XFEXnzxRebMmcNLL70EwEcfBQ6ff++991JaWkpcXJwWd6VNG1ddbHk5fXbvYW8/73BLE/rewPbVh/m/+ZehN+p48xMtqu/Qq1PYuhg/geMrbgBy9KXk6EvBCNN2nuTzAZrzrMFuY/rOdegddsIsDSAgMrEP5uIMetcYGZ5t4ZqMYn4oWcbbHbVloeic1oigYspbuiNMmwpOYiwvcn1FIoZHEbwZUmlDJ3Nr3gl2L+NdYemL9SNtO/ztaJnnWz81lOLs4xiKIaJPa4LyvocP/qAFp7NXwRxtyU8A4Y+W40jMY8c32Ty0soynpsUgdYL2pVZalNsZKOHVVO075hCC/BBtVp7i5vbVWmiCty91WyS/XVdDjFUCh6gD6oD423oSnBqDlDLgr1oppZ+4Ac5K3AijjugrO2GID6H4DX/h1uapEQid4E8DknFY7VQUl2M4Ws+BlTvZaExneO+hrE3bBDoHZS3c6SZaxremsOQUYSERDOgxjF37t9GqbTwhnUL4y81jOX78BHq9gaVPuoXouJkptOuSQEhICKWnajiwIY+4rEpKt3g7igsJD35ejlUPWYlGPhvuLSo+GNWVQOgcdhw+ObGKI2MojtRus6udQdQTqsu5blMZ1rADSCE5GduSFRe5l8Fv2/w1RoedEGFFAq31pUwLrWPMmLHsLihk3NhLeGvpRsqP/uDqI9G+F/pT3j96JdDNlkQvezsMmCgSpawJ2u9Vbxc6WtrDKNVVczK2Bcsv8gtQ78VXfUdh0gkemdqarLoGrjl6gsJRrTkVfyPv9uyIEAKr3UFpVSXHMHL1XvePoQSjjq66Y9zR0MFrzNYVxYSb67koL4sEZyT2zIz9EAy70LGUyRCs+US1cEQx2RJPhW2m1xhGcYy9Ygc9g7OJYi8hdkmxZQE26X0PlUhO6cqx7SimnSMewUSqmIiwND4O/uRq69nXIHJpEXQPVtmBMvtg7PbpXuMW4uAZ6rGjxWHpi56D2Nnh8QQKAkZhpBOCGQR+ZugpIdr4GoIG6uxjCNFvpdx6Jw7iENTQ+OiXPv2DjN+xq09nPsmqp2vNdubJTAplDAV97+G2S3rx6FcHePlwES/TwNUY6Y+BZRFgskqChOBEsOCZ6d04WVDE0fwyLs//LzFVR4lpOIY+pjX2vrdgLNiNY+QcmO8/73NNcy04XwHLpZRvCiGeAaYC/0OLKlwkpfx5bI7nEU8LTvlH04i5QXvAVxPBTPEeN8u3mcAK5jc8SVqwdnNqtOCMec0ryTkfyakArGU874iZjJJreblXHxLiL3G1+Xz+Tu4apH3JDg/vSfRpfhU7pOSNpc/zrSOardE/HlNxUe9OXLsvK2DdSzHVTPtqEhWGCGJsHnlO57hj7kiH4/+zd57hTRxbA35HkiVb7t0Y22C6MWBqaAECpJBAOklIbgIpNwkpN72Xj/Sb3m4a6b0npJAeCDWN3jHdYONe5SKrzfdjZUuyJFsOkDrv8/jx7szZ2VlZ3j175hQ+efgeBh89lezBwyjens/bt10b9L24M/EAACAASURBVHznDCgi5fT7oK8WBfPOO++Qn+9vnQjGiSeeyJAh/tdV8dxcyh/3ZJRtDI9g8YSbqIvfjMEWQ3xV4PD7y56bBEBZUTUfPLqcSTP78d48T0bbmOr+SJ0DUsuxWP0zH9h1enTShb6d/40hxiRGjUvg2QUBcom46VKVT32phdQevSjdtYOr3/4UnV5P3ZK91H3pyetTKSwkyCif5QTz6ZmUNRbwxf8e8hnz6PRSvt+fgjANITm6L9WOdMBFrKGcWkcSoO/QjF4aq+f5Kb5+MhkVds5dYGHMyT3J6BdPbIoZU4T2naz7voC67zuX8yVyZBoNv5QQ1iWS1CuHAlDy0AoclVbMg5OJO7mXZn0CpMOFdEp0Jj2uRjslj6/GkBhO8r8HIYJYQKSU2IsbMKZHBexvS3l5OU8//XTHggFISkqib+IYlud/5tM+Y8YMqqqqsFqtJCcnM3Cg5lRdVdzA/u019ByajKWxhmeeeab1GKcQfD1gFPsSfPMh/be+CGNdDbt372b06NEkJCTQI3cAP9c1ct6G3XRs0wid3KJd2PUGJuZrEYMNxnAW5AynOE6LlEipq+LU4jCe7etfRqSFe3VRZC4vZefgBO6MaA4qF4h+tU62xv7+hU0T62upjPL93meX7yepvoZBhTtx6PUYnE6W9RpEfpduAcc4eu+PVLnisesNrMvsHVAG4K6fd5LqSGGttFEdI/mhZwwxdYWcUvYNG0UPltTmYGvSk5IQQVeLg7lH5ZAyumvr8uw1769j3poiZk/oydVH9cYlYcXuKp5auINfCqqQcUYcPaNxJYajL6hHV2PD2DuWY+Jhav7XDBg0nFKbiTCHhfnFseSXN5O/r4xuooQmwjlVv4QTjCsQUhIfZkNn80+hYXNbOi0ijlFjXsHizq2U3VzHVTueIrLeQnXpWOqaxtAlYglTo5/jwZ6zWB/Zl2N37SQqqgprYS5Gl424pgb22oZi1lVjk5G4Ymq58IHz/jRLVD2AKCnleiGEGXgEGItWQPMad4mEvzQ+Cs6bpxJ/trYU1UgEF4o3+Zd8leP4nPua72dTuPbFfuzRu3gjbRKrzzrcZ6wWBWc543hGXAXA6n47SO8yHYCPSqrY/NB6np6mLWv9MiqHbhHBTX7LquqYvm5X0H5vBtdt4esTz6Ta7uCDdcv4P4tn6Wx/9Fp0w8+FO3z/yelzLLidqoNRtb+QV67W3nZMkZFk9h/EjhWeKufX5rizKN9SDEYzzc3NPPbYY1it/rl4Wrj00ktJSQleG8peUsLe8y8g/aEHMSQls2PCBFxCz4KJD6KTYYgAJdCmXDyAnkMCj7l+/XoyMzNxNYYRGQnh9VvZ8/x5FDWPoNA1hihjOAX6Msp0ms/PSc0jiJdR6NH5LTl1xDWul4nRedUci+kKx9ynhebWl1D//pPU7DqRZaUfU27dR7q5J0dMnUl46n5eeeoFml2NwQd3Y4q7AmfzRhxNC33aDeFj0dnXcNTM0/jilddaZYXQFIqZ940hPM6EoRNOgtIlsZc04GqwU/nGZqQttEdu1/sORwSJQPwjsNlsVFRUsHjxYvLz8znqqKP47jtPwVCz2UxjY8effTBuuukmwsLCWLlyJV999ZVP3+zZs4mNjSUiIgKnlGxrsNLHbEIfYsi1lJKyhjJOXbGUHfTy6z9RfsitfXKZsjObqg6W4Q413w3vQ7f9+9BV1xI5fCj2wkIMXdJxVNZR83kR1t11PNnHxAdZYUQ6JMcX2blgl43xRwZWqp5a2UjfOhdvdQ9jU6yeFYkGPlxaT2ajpFkPyzNNbBybwpc1FqodB5j75w/ghJQ47u3dFb0Q2FySn2vqeXRPKdsag98/O0OCQc+iYV0xy0qiIrXnl5SSfVYbyyotLKutJ86go3r/BubReYfsrPIG9iYHX0VIl/vYLzKZJufhQsd3TKFw8qhDruAgpWz3B82WdRyQ2JHsX/kHzdIa8Cf6mtvkFQuuld8v6CF7zr4qqBwgn9z4hfx+QQ/5/YIeMr13fFC5iKmnyNSFa2TqwjXy3cXL2h3z6rdfb5WNmHpKULnYHt2kfU68lI3VUjbVtDvm3Af/T8o5MVLOiZFzrzqhXdkWnE6HHJyXF1TuwqFhUu5aLKWUcuXKle2OuXLlytZxL7zwwqByQ4cObZXbd/nl7Y45Y9zV0tFgkbK5Xs69+PD2r8l97XJOjBzaRRdUblr/MXLhJY/L4hu7yBsvOafdMS+88EL51I3z5P8u/l6O6Tc1+DV10Uk5J0Y23Joo1/ynb7tjzhzZVzbemiCttyXI6cMGtiv78OlTW3+6xse0O88WDtbfaWBqH1n5fr6s/b5A7rtxSfvfvblzW8ecO3duSN89KaUcOnTo73pNQ4YMkQUFBXLOnDlyzpw57Y45bdq0Vrlp06YdlGs6bmq0XLv233L1mlnymWe7tjvmM892bb3vHDc1Oqhct9zerfeS1IVr2h1z+tVD5LcLesnPFuTKq65Oale2qrBGWu0OWfbiS7K/yRRU7uSEVLm5bz+5dfgk+cmRs9sdc965L8h9Ny6R+25cIs/KOz6onPc9wmLZ2u6YN2Z1k2sGDJTLhh8mb01v/zN95/TT5Zw5c+R19/5XZiYFv/7x2T3lvVdfK+cdf4J8bMzYdsdMeeo12fezxR3eyw29c0L+O6Vddq1MXbhGHvn6hzL6mtvalW0ZL23BSmnonRNUzvv5lPDcW+2OmfDcW62yyVOP7Mw1rTzUz/UOfXCklA53FfB+aAUy/5G0rIA2O9t/M5qe0MTmUm27CTPQcfLnemf7bxyv1ekJS+t4jtlNhRiIhAe6aXkn2iO2K7TkqAtgngyETqdHpw9uWnZKAcuf1EIZDwGpt90GTwUPUx1/fDz6B91vH0Wh1YCt329CxqdCceBInHLrPlJNT5EWLTntuH/xwLNvBB3r4osvpm+PXF67+cegMt6YDXYGJ7TvkzImrZS9Y7/k+0+bMJjnA4HDeAGufW8+C15+jrXfzG93zKKtm/jw3tspWL+GwqradmVbaG4u88vb5E1Y1ygSTtMczmMmZ/0u6+uHGoejlqamlxk3/g3SUk+iTVULH6ZNm0ZZWQFO58HN9FtRubBjIWDE8E/IyChi0+Zr2pUz2gp4yW1hzqcvl7Ujm1aXSq+kL9m8fSZa3eLgrM4fCvlANtizJAQJbGvIM7J06Bx6Hncr+7flw/eB5QB+qrNTZNL82fY6glsyGndvYtFrfXBmdmy5CXPqsCdH4BrmpKG5Gf4XXLZ779X0W7EWY4HgS4slqFxWciInf/kFAI52LNYAV9Y9SUppNkWrc/iseA+rg8jpXS6mLlvI+cLKlqRELgwiBzCh8kcO31hFj+4b+E7uIUCZWD+kaH+JcCyLSZGHMZVPqZV7gyYWBbhSPkSY7I8ElvMLXwaRi7Y20LOskCajicO3r2/voz9ohLpE9Qtwq5Syna/jXxvvJSo+uAxO09brJXAOH3AiH3Ea73J11btUSCefPjuHtcRy72GzOPekfjzXZGHl6P7oqj5ly9abAFjBSB4XN3R47lcGdCcnKoJRP2/hrl7pXJSpLbGk/eCfh6CrAc7a8TLfW3I4adf7zDnxMQCKlkxAn3sqbPTK09P/RMZEncGuiK78MKIvvcOdLF6SB8Cwoe8R97jbJ+jGAogILdy1LduWfs/nT3n+pa7ptxRxZ2gPzYDYreBoggh/33UpJVtz+rfu91u/DvHZbO2a886Ede/4HROUlFx2vFyOvd5ARVQEW9ITsUSYmHH+vRR9sZqfy+djdzVz5t0PE29zsmf69NZDe377DdWJJpLNyXy+83OmZE/BpPddYnx6dvsPppOu6E/XnFQtm+09KXxSfiU7K9YCLowxs9DpEwMeN/upI9AbdPwy73127XqamG4VTDj6a6JifJ0gf/xpMk1NewDI6/8Rr115G9q32Xe5KG14OWnDtOSKWz/IJra7hdLVSYBg3FnnctiJ09mx4wEK9j5P165n0a/v3R1+tIcSp8vJZzs/48huR9LsbMaoNxJpiOSRn27m+J7T6JU8GoNOj9VaRESE5zOx26spLvmELmknExbm+a6vXHU6tbWrftNcMjLOwRyRzb7C12hq8vhUhYUlYLdX0T/nQczmnthsZSQlHYXVug+jMRW93kRzcxkWyybWrf+3z5jxLxjYNcWFI0Yy4EEDO89z0pAdQU7OI/RN6I3JlIpe3/mCmMXF89i85ToAhg59l107H6Gm1uMErrMZGDn4c8xpfbAsWkT5408QOXo0xm5ZRI0fz843vmJD7K8k9Pl9HwF7f7iOrIkPd+qYop8uQh/WSNrwECrJ/0YshUOwFA3GWt0NW11XTLGFCH0zIEgb/gbhce1E8wVg1cppNDXFoNM5MRqbMJtriYquJDFxH2ZzHSUlvYgOc+BsSKZ869E4paDPMfcghP/z29lsRkodujAr0hnG7m/uICyqnLqaFOxheyhNshFps6LXOXG59IAgq2g3EZZm8vv1I62kiNyNmzDYHdiM4ezo3ZMssY29WVlkLKsgpayc5CMn0ti4l6r49dh6S+Le0qOvEugafe8vOrOZ7h+8jy4ri3Xz5/Pzzp1U1NczY/p0cgYO/NP44BwL3A/MAVbhefcHQErZca75PzneCs6yxedw+ATPm/os3uVY5jODN7m25j3K7HZee/AGdsRl8MCIs30SSRUXf8TmLZpSU0sMl4pXOjz34/0yebmogvUWLSCvpXZVIAXn9NR4ntx8J498UEZ87xqsk2KJoIkzfl2D+bjn4WPfG+a3/S7g+sxzub/5DIz4WzXCRSxjJwZ+j9hevR2JpLa5FovNwqSsSQHlHjnDk7BrVFIBY4Z0QWSNhOo9cEZwi4cfzRb4rzt66o5aqCuG4nVahtTeR0FDBbJiF1uPmdV6SHzveuyNejLHtbGUDTkbBv8LUnPhs//A5k+h21jqxESK7n+5VawoPop1Wf4FMLv06stJN/4fjl9+peg/V/j1b86EO872GEB//devvLn5TWKMMSSbk5mYMZEtPxXzwxtbQ79+4F93jqJ0bxmFFk35LN9wInpjA9HpexgwbCZdu56FEILSsq/YuPFyn2MnTdyOEDq2bL2V/fvb96nqiMotcexblsbp917Ktj2+15+VdSFW637KyrS31l49b6Ck5FPqG/I5fOyPmEyBC4q2x6rSVZz79bncNvI2zuh3BqAptC9tfInn1z/Pu9O06znxkxP9jk0xuLilS+A3Z5MpjebmkpDnkZZ2EhbLJhoaPCaIwXmvsnbduZ24mt9G/PMGItYG98dZcFYfzr/5HcxhZlzSxbA3h+FwOfzk9ELPg+Mf5OjuWuyHdLmo/uQT1g+Motf8DVS98DKFY4YQs2YTcc4mHGaBo96AyeFE55K4dAK9K3DaPFsi1E8wYPohGl2jIKF/N/Qjsok+/xT2b99N2e4kXMKO09xI6SYrjWUpfDngFv6TpWXjdkotFcrGvbn8UB3JrD6biIkIXO6kM9TsOhxL0WAaivP8Pw9THUm5nxHfa3Frm70xnqr8YxB6G87mKJJyP0e6dNgs6ZSsmInBXE3G4U+C1GOI+G0vbCWr/kXNzgm0vFSYU7aQ0PdbIpK2ow/rnGN2++gwmVJITT0Fc8JE5u9bgc4ehn5eT2SGhVNmDIOlKyi/9z6ctf7Xsi8JHHooTBSM2+yvD5TOPoEBM69ge8MeaquKmZI33U/GWVuLq7ERe1EREUOH4pBauoR62cTk9ydjdWr/nwadAYfLwWFph/HylJf/NAqOt0eh9wECkFIG8Pb8i+Gt4Cxdcg7jxnsezP8SH5Er13MLd3JV3XtUNtn47s5/s7jbMB4cMoNPLhvL4EztrbC45BM2b9YijnKGfsHQNR07iZn1Ohqdno/4GXsJJx15NOmLfUNiT/3ydS6ZOpV133xOya4d9DpxD1FpmlKUXmyld87dOL+6hsoEI11Km1tvUDtn3sGePcGXdloejN44XU4Gv+EbpfTh8R/SN6EvgVj33Zd8/6IWLTKt6xb6xnhVDr96E8RmBDzOh7bOz96M+Q/8qBk16/aFU7Tct75R3+nF6AwSLloMaYNaa+VIl4ut/bXY2qgJE6hfvNjnuC/zAue+mSR0OB+6hcijPOGeUTdfQ/1/PTktHzpFx4q+gR9Iy89cTowxBofNyd03v0FKg5YqKm1mE5Ebu7Fn83YS+31NWGQ5JStnIqWOhRmfc930cdTvaWctpBP07HEtO3c90qFc/5yH2Lzl+nZlosx51DeGlhtlcN4ruKTdJ2owEPlV+dz3y32UNZZRWK+98eqRvHLsG3y1cz6/FrzD9Hgb5XYdb1QZcTq0b7RLDz1NTv6TcnAeEkOHvE19Qz5d089Ap2s/v4fFWoG07afGsont+be1tt+1P5wqp44oneScxGbCBPQwdeyIHT/XQPgGQfXwPryRtpPCJMFDL2v3oajbb6D+7gf9jtmYJXhhio4nnvcsyTw9Vcfy/gKHXiAkRFolfQoleTsT6FYmSa5rZEXP9FA/EgBSa+rJqqojydKEqVdPDNNPwLp2LT2uvxVDWhrotEzIFU0VFNQV0GhvZO76uawrD/w9yU3MJTcxl0HJg1iwdwE/7PsBgOuGXc2RSUkYZXcSkvsihA6dPozy8u9Yv2E2WZkX0KPHdej1RrdvhR2knu/e/A5LSQKHn55JvtzC5UuvbD2X3imItXZh4s6zsJiq+LbPKyAkeiROBBmlEeTtiCW51vfv7egWy7/Ou53dDfWse6GG5toXEHoLcd0t1BZEgRREJFmJ7W4hoU8thoi2y2J6emRfTVbWLAq3NrHl5yI2J/xE2d46qsJKGVI6mXhTPEajgdxxGTTW2YjO3cGuzRej1/l+XyxFZmp2xuC06YgYWEWTTYfF5GSg+3u/aXcUtm8zqIizsTG7jrpIOzVRdnRS+w44DB0/2/9INp678U+j4Exor19Kubi9/r8C3grOurXHkDfYk2itJbnfNfK/vGq5AVN5CW8/fB17EzO5eNyV7Ll/aqus1bqf5T9qCf0OP/wXTttYza+1B/6GAnD9c7f57A++eEsQSRixuoaIJicGp2Th+KSgcgBjxy7HZEylonIhSYlHIISeZ9c+yzPrnvGTfenolzisy2F+7VLK1oSAubGlTElvEz59yosw6LTgk6gugCcGtTtPbyxVXSn81vPd1YW5SB1cR9wbxdR8PI/iW24h4+mnqHn/Az+lBqAiCn7p2aM1NPvKF97m61mns1vAxC0F6L3+LVwCZtzksdYcucbFRV9rN6PPTkrlrb4VrbXKhm9zccNHnhvV7ptO50bxcet+ot7FULOTqXGeXDm/lZiYIYwY/iEOh4XFS3yV0ezsq+iR/R+amvah10eydNkIhDASGdmT6Ohciou1NAjjx60iLCwOp7OR8ooFJCaMZ/v2eygu8cy5oTScHfO7kXeBb+h/0c8pDJoyhMqabwhGQsI4sjLPx2avot6yGbujjuLiD4lJmMB9W39iR7OeHiYnV4SgrDRVmjDF2jRFtg3p6f+ia/LlvLX3fT7a/CylDh2nxtkYF+3gg+owfqo34ELw/ckfYXLVUVu3BrO5L7tteh5f8zSbKzdzw4gbOCvnLJ9xbU4b68vXc9435/md05s7Rs1h5As/Y5n/RWubDDNQldOFxPX7cIVrc5YmEA7QNWjfl4YLT2H4tff6jCUdmlVGGNxh9FJSvXkdpadqmcidQqCTWrYunXt/Q0Yy+xOCh3W3JdxmwxqgHEpHrO1VQ116GOeLY3mh4WMmrEpE565EXJJg5ef+VTSbnPTbE8Pm7nVENRl45NTnGJYxAgCX08k7/3c9xggzx199E1JKPnnwbvbn+xflBZh6xfV88aQnVUJqj970GTUWc0wsJTu3s+67YB4f/sSmpFJb1r4v0cFkW4YFq8lFdIOB1GoTBalN7OpaT0aZmaoYG5Ncg6nfV0xk1cFMAtA+ywdWUh1toyLWRkSznkx9Klf0mc34cSex5K1XWPPtfDZ0qaA+xUD8wN44C6vRlTeyMmEfpshILPbgvkjeCBcgQAqYnDWZaT2mMSFzAjuqd5AelU6hpZAByQP+HArOPwFvBaeuLokYLwtE2/pTI9au48G599NoMHHqtHt9FByABQs1q8DkSTtZVm1h+lrfnDRH1GxgUdzATs0vuaKYcz/0zuMhGXxx8OWP3NpebIr1zxQ8aNDz2G3VbNl6Y2tb3z53kb/t/wBITT2eAbmP88CvD/DmlsBr2BtmBXZ0bayt4dmLzgag+6DB1O9ey6yMpR6BY+6D0QHcGu1WTbmpL4Xex3jqrsR3h/HXw6eBXSGb6/SUrYuhvih0n4SyWLj8UgNj1yfSu1DLofLlqBL+O+MZLvj2AnL3uJjzju8N56F/mVmR5VneG91lNDe90UjzCl/fDTmwL2KDRwmQSJpzJFX/caAL70FVwz7i9B0rNhUOwT3F/teUEB7PK+NuoGDb9QwY8JSPhaSxcTc//XwkAGNGLyEi4sBq70jpoq6ikJrSIha//jblBbsBSM7qTvnePT6yl7zwFuaYWFwuOz8sCpzA7lBgLx1CdvZ/WPTmXOrLawFB3lHHMumCS7j/1/v5Zs83VFk7t3p+bPaxHN3taG5ZdgtNDt8q6uHNknGbJJuzBGnVkhs/7NyDaUmuYPwmz/225zdfY+zWDWt9PY11NSSkZ1BZuA9zXBwRUZqy0lRvYcmbr7B5yQL6jZ3Avo3rsFT9tliPXiNGc/w1N6FzJxJcU7aGNHMaXaK6UGero665jhuX3sj68vXM7Ho6kd/tpWnPwc06/Wei98gx9Bw2kojcLMwR0VQ0VTDn5UsYs8pjSS5KauL7EWXIAOt1/cN6sqNhN+E2HS4dnLSkCwbXb6+yPvPhp4iKT2R17XpSzanEmGKIM8RSsi2ftd/Mx2AysWXpD63yWQMHk3fUsVQV7uOnj97B1UGwysEkJjmF0dPPIjY1jZLt+cRnZJKc0Y0X/3MBBpMJR3PwF5Z+Yydgjoll0nkXKwXn98JbwbFYEomO9txE2io4E9Zs4Y7n76IhzMD0qff7KTh1desxGpMJD+/C2rpGpqzyWDPekqeSX3wWd6Wf2qn57R7Tn2fPP4m8C/IpXpFEXM86IhJsREcPwGLZ6CfftetZFBV5ClLGxR3G0CFvIYQOp7OZRYv7+x3TwuRJO7n2h6vIr9nBRyd8xFNrnuLVTa/6yNw68lZm9JuB09nE7t3/o1u32YSFxfj44wAckbKTYYn7PQ3HP6lFcPU6Esq3wdMjfE9+eyW8PxPyv4DbysDgNiGXb4Mf7oHjn4DPr9R8atzsWjuJ5q3t+LqMyMMw4yTe/+gu3p2gR7hg1tfdAFg4tIy9ab4PMoND8n7+UTg++xqAfhs3UGWv5bXNrzE5azJ5yXnYS0vZMeGIoKe0dXNRcaO/f0QLpk2CqO+1B03tKU7i3tZTcaMDizTysW04a8oCF7rzJjM6k+N7HM8lgy/pUPZAcDmd7F6zkricnhgMYSx8/HF2rfbPUrxsYAU7Mhr49KRPmbfpMQY3h/52HR2dS06/B9jw7WrWLnyZ5hoTLof2sNAbnQw8z/M/VLgslZqdMTisHSdizzt6KhNnXYjUwZA3/JNJxtWFoUuOplt9HDEb6/g1pxqr0Yl0P6fCmyWT10lmLehYmbGYwvhlQE9sDjsZUXGMTcpg+w/fk332OXS77HKEXu+T+XnVF5+w6PUXAw/mrgvXGQZOPoZxM2ZiMJoo2LgOY3g4mbmDDrh+UsXePZTXlmKSBubdO8enr//ko5h87myq9u1l4cvPUbyj4wSfmf0HYjSb2blSK9jb+7AxTLvqRp8ITUtlBYVbNrLgpWcZe8bZ5B11HAUb1vLZo/ehNxiQLklEdDSn3X4v0YnJ6PR6GutqqSsrJTGrGwLB7nWryB48nOaGetZ99yVh4RFYKsvJzhtG9pCOn6s2p43NlZu5++e7GZIyhPMHnM+asjVkx2bTP9Fz/3S6nDy88uHWF8KZOeeQWxRP1YrNmuLvdJJ31LGs/HweVUX7sDZoUav14Q6c47tz5/nPHrKq506HnaqiQhIzsihYv4aP778jqGx0UjI5YydQvGMbxTvyW5WU6MRkLJXlB31u173/hVJwfi+8FZz6+niiojxOq20VnMkrN3HbS/fQaIJTj33YT8HxZkt9ExNXaP/0T8iLSaICZ3MSz+1+jB9zzB3OK76mguO/f48Hn36BvdsWsb3wAp/+5JQTuHrdt1yVasesC67Bjx+3hrAwTxHPysqlmM3d+PGniUGP+abWwIMne25Yc36cw8fbPyZCSOIMkhGJPZkUtgkAgyGWw8cup668urU4ZwuDu+mYbG6zTJQ+BKp2g7XGt/2OEBz6pIRt38A7Z7QeI10uyh55hKqXNAfily7vxQVP7aA0Dv5ziQEkJFjCGLkpgdRqLYR+4NFTyJtxGkd+eGTr0Gf0PYMz+51Jz7jAvjlSSioqviMxcSLVr7xB2UOa+Tx80CCs69fjjJGU3h/cShP3sh5DqcC4L/CbXr+NG7AsWEjxbbcho8w0XnI6bxV9xvSjr+aNV69haa7wqSIsXJIbP3CxaJDg8mvfpqqpisMzDidMF7hyekdIKal65VWad+4gavwEIo+azBnzzyC/2vfBdeXQK3lqxZOc803gMnR1ZjvfjSjDEumr5N3T5SKinv+GtXo47vpz2Ff8KJs/SMDZHNiNr6tT0H/zLgwuV6tPmUvA+swU9sd7lmTGxaTQ6867WpNRtsXcbCOrvpm0hGRqZp/Aju9+pH5Xx5EuE7YUEGlz0KzXoZcSh05HSWwkmzOSATh8xkyMDicLPwxcgqSFYVNPYvi0k4lK0KLjVn3xKYtef6HD8wMgBPFdumJvasQcF88J19xMbEoa0uVChJgk8I9Gulys/uozMnMHkdK9xx89HcUB0FRvYfWXn7Lis4+I5K0SkAAAIABJREFUSkikS6++bF2u3d+Pnn0F/UaPx2Ay+ShtzY2NCJ2gqqiQDQu+QW8MY/J5s/9eCo4QIgF4CTgaqABullK+HUDuDuBWwNvONUhKucvdP9g9Tg6wBbhASrnW3SfQIr5awoleBG6SHVyot4LT0BBHZKTnwdtWwTn6lw3c/Op9lMfAS7Nf47Xz/X1SWshvsDLhV8260JLhGKB47WmMnX0n88truHdXcDPw+e8+QWJNOde+N589BXPZudPX6bBrzvOc9u1VCASXDPo3U7ofxa41vlaU9PQzyOl3X8DxW5bTAIrtgi5hno+pzC448xjPMle9rZ7R74zm8czAmV7N5p6MHvUt7865gaKtvmvq115xNHx3e9DrZMKNkHM8pHVi6W7zp5qiFOd5yDY01jL59bE0RAi6VEpK48GlE/TZG8WYjb6h15e99C7hUVGsLVvLtwXfctHAi4gLbz9cvqT0czZt0rJTm4ypjBr1Da7iGoyZmVgsW/h1he9nPyb1E/acejpNeS7CigSGCkH2p5/SsGwppr79cNVbEAYDdV9+Rd2XoVk8HrkwiUErKlmWq+POtzxK7X2n61jbU3vgTciYwIPjHyTcEI5OBH8I1i9ZQviAARgSNKft4ttvp+aDD/3k3pmg4/vBAovZ/02zR1EkydUmcvZ27AMS09hMnbl9R94jZl5Ij2EjsP2wiKo7Ox+W7tAJXEKwuF8WdkPo8Q+RRhMNtgNzXB563IkYIyL4+aPQotjOe2wuCem+y4nS5eK16y+nubGBf//vJfSGUEsGKhR/HYQQfzsF5x00v7gLgMHAF8AYKeWmNnJ3AL2klGcHGMOIlkbqceAZ4GLgWqC3lNImhLgYuAaYjBbx9R3wpJTyufbm5q3gNDbGYDbXtfbdxd3kC49J8rif1nP96//loVN0LHQ92K4FxyUl6Yu0qIKVfTaTn6895Cu3HMvplz3FytoGpq3WQlIvfe8JnjlDiwQQLicj839i3OKvOeaSqxhwxJE+ykgLlqxHuH25r+LgrYBMmrgjqPmzJVIqTu8i2SDpZnQxrY3z6+RJO3E46rHZKtDpjOh0JpYuC67QTZ6k+Rs5HQ6+f/FpNv6gpcEfc8p0Rm9xRzl0GczWyO0UpUcwaXdPxAXfBhuu0wx7Yxg2l+Yv0z2mO3vq9gBw7pfdfORmz32DyLjO14kN9DeIiupPfb2vQpfb/zFSUqag02mOnNbNmzFmZ6OLCOwv5KxvYNvwA/9ff/xEHT/2D6zQPHbEYxzZzWOtKv3v/VS9ppVy6Lt2DcJoZMekyThK2g+rXtM3DLt0cNg2ya4hqXzQs5xZSVMZkj2WvTffQnVkOCt7dGl3DJPdQbO7/tqELQXsSo6jPCaSCVv3+tUAiz/nHGKOPRYE2AuLiDnuWNDpkE1NFN1wA7oIM3Wff+5/Er2e2BNPZJfVwi9F/qVOhuwpIa6xmSajgYQGT7Rj+BET2LZnB7tNOhrCAzvijj/7fOxWKz99qL2fHXXRfxg0+RgfmZblqOId+bx9q38tt/Mfn0t8lwPzlVIo/qr8aRUcIUQEWi2q7VLKgo7k3cdEoqX1HSCl3OZuewMoklLe1Eb2DoIrOEcDrwAZLVYZIcRe4CIp5ddCiB+BV6WUz7v7LgAulFKOam9+erNZDugT+LMoIJtGPMtJibX1ZJbuZVOWwNnYg1E9Aidla2Fvk416p5OcyHBqarVqxtKpJya2L04RwSqLFbPNSnJ1GQWpmjUipaIYk0276Wb216wa1TW/+IwbFdWPtRX+xR7TImKJktWU2gV9k0b4KTg2p41dtbuw2Dwe8XnJeRTU7SaRGu8VEOLjRlBd4+9v0UKlQ+CQkBomsbsgJWFka590uago3Iu1XjtP127pSL2ApkrqhOb8adLHYY4OHHreFiklK0tXAjA0eSj6AFmVV5Roc02LTCMzOhNrQ32rgyxoTrLhUVH4lYAPek4HNbWr0OsiiIjIoL4hSIpWL+LjRnYoE/BcViuu5maEXo8uSnOAdpSWoo+N1XIzOJ1YN23yPchgwDxkCM3bt+Os0ayOjQOy2e1W7FowN0N2aWj/66VxgooYMNmhT2MMOpMJR1lZp65FhIdjt9kQ8XHIhgbs0oXR7qTGbb1JjUvAXqT5Zol27kHG7t0xtFOvrBWXC0dVFc7aWkzZ2a1pAgIiJbaCAmRzs29eEL0BfVwspp6+SqzDZgMkBmP7lqdQsdZbMEVG+qVmUCj+SSxevPiQKzgh2T6FEK8Cv0opn3FbUH4FcgGbEOJkKeVX7Q6g0QdwtCg3btYBwULQjxdCVAHFwFNSymfd7bnA+jZLTuvd7V+7f3snYljnbgt0XRcBFwFB36wDHudzP+74oZEVob0Flu+1YHC7wQi9E0v9ZsLC4ukfZqChtB6XUzAyNpLa/RtosAV4AEuwS80FwyUhzBA4b0xJUy2aoQyqrFUkRvgqYG3zVPRN6ItRb6R3fF+KG4opqy8kOzIap6M2qHITFzscu6Meq62RQkshRgHxesnKkhVIINYUi0DQK7MXJbvWYYyxsbFmD5mRDh/dotlZQ7h0+SyjWB1WTAaTT3VtgC1VW4irDyPcpmd/1WZSs3thdP/dpMtFWZPmCBdljCIzOhNbU2MA5Sb0UFoAu117ADpdTa3KTUz0IPT6CKzWQpqsRa2ykeZeGI0JAccJBREejj7ct8SGIVVLmtfySYTn5iLcip1t716M2dkgBKY+fbDt2YOjrIyYknoGZA2gzlJBuaWE7uUCnTOwk2xVjI6EOt++aneB7j7pAwnXa/Mxdu+Os6YGV1MT9n37tDlFRCCb2jhop6VhzNKU9ED/Ud6fvtF7aUZK7Pv3a8qMToezuhp9XFxrqHSH6HQYkpIwJLWfEkGbuMDYvXto4wKG3xBO3R6d/Q4qFIrfRqiLu8cAT7q3T0C7T6UB5wN3AKEoOFFAXZu2WnzveS28DzyPVgBlJPCREKJGSvmOe5y2nqje47TtrwWihBCirR+O28rzPGhLVI8+GthJ9w7XHLbrPVaGU35YRbV4lKYeOiz5d7Dovo4jotZ+v5flH+4gddhrxPdcFkAinrVzc7j2vflwRyyPbBnX2nPte/NbI5++rA3j2zoDOmDRrEUMfK1jn5VFsxZRXF9MRVMF/RP7+yXw+/SUT8mM9k31X1m5mLXrzg865uRJS332r5vXj2Nj7Wxs0vFihech3SAqeSZDU7BK1yaQOtg/bNfmKiKn9w1kZfyblze/whOrn6BRNjAldgITHYPo22cY6T378vQFM3yOc4YJdqVYcOkkfffF8P0wK2HJPfh0ykdkd+mjRXR1197+c484kimXXNXhZwXgdDbT3FxMnWWD29/GkyAtJmYwI4ZrPllSSpzOBnfWW0Fs7ODAA/5OeJeyiBo0nPqFC8GYRWtx4OREvp3RC/OCXxm9RfLmJB1fDxMYmuGlJ5xsS4fnL+/BSV3HMq3HNAYlh56XSKFQKDrDoYoc8yZUBSceaLFRTwE+klKWCSHeRXMGDoV6IKZNWwzglzlISunt0PCjEOIJYDrwTgjjtO2PAeo7cjJuj7ZJy3VSYg/T2oQILffA8g81Z919ixOIDxygQ0LXTHD6Rp3EZtfx08/HYG3WzPkDIxx8W6dFyHy126NXjkgb0bo84zd/KTnhkxOwOq1cONC3bNuQlCF0ifT3l0hI8C2YOXbMUoTQs2z5GLqmn+knf8nYF9mzcRYDInyTXj+Q4XnDb1Fu8q06tjbqGfhrKj2mFGLUwc6dD/LJ/83jw8NKIQpmLuxKz0lLyF+wk822zwJel94u6V0U1bp/5CpNmfn4a9+Cg5e9/C7hkVG0h5ROXC4HpaWfkr/tDlwuX2fThIRxZGSc45N7RgiBwRBFbKx/+PEfgRCClOuvp+yhhzTlxouujz9OzJRjyAG4TPtOTCz4li8WX4fFDKffbKBnbE++OOmTP2TuCoVCcbAJdRG4BBgghNCjWXNaKq5FAaGmZN0GGIQQvb3a8oBNQeS98a4SuAkYJHzVv0Fe42xyj9vZcwRlh76Pz/7uLok43O4fwmDhlqW30GAPLVuxy76dHfMDh9YmZWRBs2bkmtVDSyKXfXQRjY07cDk1x+Gva8MwGzR/oLnr5gIwNGUoLx/zMif3OhnQnGu9qbJWtdYCeWGDJzT1yYlP8vqxr2PQ+eu5Qgi6dJlOuCmdSRN3EB6ejsmUyuRJO+nX7x4/+Z4ph7du3zhEU6LOTQxc0dvxUQ+6fdCLugJf493AWdu5M6eO877JZMi524nJavDKf+LRT3sMC+7k3JYz737IR7nZsuVmv2W33bv/x8If+rBocX+2bL3ZR7kxm3swedJOhgx+tcPSA38GEi/wWN3SH36YnK1byNm6hZgpvg6wQgiO6X4MG2ZtaP35RCk3CoXib0SoFpyXgfeA/YATWOBuHwmEVE1QStkghPgYuEsI8W+0KKoTgTFtZYUQJwJLgBpgBHAFcIu7e5F7DlcIIZ6D1kryLa+srwPXCCG+RHsqXgsHtzK7Ux9GS7X57r2W8/muFfRN6Mus3FmtVaTHndGHQRO1+kv11doDU0rNOmOvD/yxb/tlOZynWUcSjY307e/vY5OXNZ1z08Zy/ZLrke6H/qtTXgXgpsNu4ojMI1qLYh4/73j21O3hiPeP8Btn/cz1HZoI++c80G5/W2Ji8qirW0eXiif4fIKnDlJlxOE8sX0Vd7mtOfZ6T46WTW/1wpzcRPbRHl+WvH/75lxpKUnx+dIUHp/zkzZGczNzb7+UGdffTcX+vVhjdCy46S4A0vvksH/bFoZMOZ4uvT2ZdVsioPYXv8/oUQswm7uza/eT7N79RMDriYzszcjDQll9/XORszV4CQ+FQqH4pxCSgiOlvEsIsQnIAj6QUra8mjuAzjwFL0VTlsqASuASKeUmIcQ44CspZcur9gy3nAkoBB6QUr7mnotNCHESWn6b+9Hy4JzkNae5QA+gpZ7Ai+6238Se3YO10bzYld6FHu4Fsf7pkSwuBKf0Xapa9dWeVgVn8TtrcTT9jN6k+cs4bYFzc1zyyvPs2P4oPQTozniTiGJ/n5EpPU5pVUx21e6ie0z31n1zmNmn4vcdY+7g3K/P9RvDoDMckvXPYUPf54dFmq+Sd5HH00e/xumj4YmZx+O0ez6nXiNGkTNuIp8/+l+gqO1wflw04+jW7TCTicsffAmApOR0GhsLqJq9EyltJCamcVTXa4mOzvVkjV3tW2Pop5/9rTEpKccRGdmbrMzzMBiUI6hCoVD8lQk5g5SU8qMAba915mRSyirgpADtS9GWu1r2/Z08fOXXAMOC9EngBvfPAbFz5zD2F/X3U3BqovS4s8jjcis2VoeV3es86aydDisvXz2bYy+9mvylTyNdVSA0y4XTFnhl8KefNcfignFJDKlb5dd/fWEE84YnoPNaWdzTJhzYm2Gp/h/RyC4juXLIlQGkDxxdgKWu0aO+b92+4tVPKVi/BoTgo/v+j7yjjiOtl7b8V/TNcZxy++UsXXgCxijN0nXEhE0sXXYYTqe2/Fdc9Cb9et/eeh67vYbm5jJ++fVYn3NWVv5AZaVWs2X8uFXo9RHUuEPsB+e97Oc8HRszhOHD/ZPbKRQKheKvS1AFRwgxM9RBpJSvH5zp/LkoKe4TtM/u/uSWFmnRRM+ue5aRqce19tsaC2iqLmTpO69pyg3gaNLSWUtnx65PBTZPzpuFdQY+rw1DIogPjyfG2NbHumO6RnXl/ePf/03HdoZJE3ew8IdeAGRmnofZnN3aJ3Q6ug/WlK6WAo2gRYm1MOHIb/np50n063sPen04E8avoaZmFWvWno2UTpYtH834cZoPzZKlAXVcH7xlEhLGkZg4gcmTdvLDolxcLisJCeMYMvjVA75uhUKhUPy5aM+C83SbfSMQBrSEyejQHIyb0fxe/na4glSGHb2liYoAq0zecVrWWs1NqXi7v4vS1Cuup6jZY0XY/U0G2cf41sWpsnvSBX1dF9YayRUdpi2d5CTksKVqC1N7BM+iDDD3yLm8k/8OT0588ncJyxNCMHbMUrZtv5duWRcFlWtRbvzazd0YP25N6xKREHri4w/j8LE/s3TZCOz2KlyuZpb/eITfsXmDXsRqLSIufiRORwMrV/mG7+f2f7h1e+IRB+R3rlAoFIo/OUEVHCllqxOCEGIqWr6bq4AW08JI4FGg88Vi/jL4KwRXf7yPKHskb+YFEHdrOA7rKqRLi4bSsqD6kpTZjSjdnSx++3+UrEqmvWSBb1UasUmv4opuJeWdqe9Q3lROqjm13SsY03UMY7r6+XEfUsLD0xk0sK1+HDreRUFb8E6gt2jxYFpcriIjezNk8OuYTP7ZbqOjB2KxbECni6Bb1r8xGkNIAqdQKBSKvwWh+uA8DJwvpfzJq225EOIq4FVgfsCj/uLomhpxRUQS12ChJlLT98xWO+jBbE+h0VWJS7iY/fMTOKIbsY/XfEdalqKC4XQ6ycg6m4x0GyWrPsYc61sTKdriwBKt/WnKHJpC83+j/89HRq/TkxaZdlCu86/C8OEfs3LlKa3KTXT0QA4bETy0ub0+hUKhUPy9CTUPTncgUKKXRrTIqr8nbmvJ1A0/tjYZGtYCcMrm2+hbPoruVVq2V4PFzPPrnke6/PIW+pGc1R2AcWfO4ow7H+DiZz2+2kPX1SBSPdmJG13aHE7rcxqn9TntwK7nL05sjK/ZTCkwCoVCoQhGqArOL8CTQojW4jHu7ceAnw/FxP4cuP1emj3ZeBudq1u3e1TmEe7wFOFEChxWT783YeEeOZ27lpBOryejXy46vZ6wMK2cgdEmkcKTDbjcoQryeTNp4g765zzI2LHL/+ipKBQKheJPTKhPzwuARGCPEGKPEGIPsAdIwZNo7++HlwvOaSsXctr8V326wx2RmO0ef5FIeyzOZv/wboApl9wSsL0Fo1FTcAxOF5YGLdHd/Jqw9g75R6JlWD6VcNM/a3lOoVAoFJ0j1ER/O4UQg4CjgJbUsFuA7w+kxtOfHZfJUw85saGOqP2eytQuZxXxFYUkN3iimHLKRmPlJwKR2rNHwPYW8tZVUKZrwGjzfJwldkFCeAKX5l36Wy9BoVAoFIp/JJ1J9CeBb90//0zcupyUzdjqXgVAZ8xBp48DwGnfG/RQc0xku0NH7N9GN/f23fvDOT7OTn6zntUz23dYVigUCoVC4U/ICo4QIh44Fs2p2OjdJ6W86yDP609Nc413CLTHX8Zl8+SuMUb/C3vDF0hXDcaYmWBw8d7kfbgEXGyrJ8oYxaaKTYTpw+hjTmdFuInbkxL5vHA/lU4dr1aaGJw8+He8KoVCoVAo/j6EpOAIIUYBX6Al9UtGKxzUxb2/B/hnKDgdrMY5betbt4UuFmjJBijY37CfJpOmDFlsFqKMUcz4YgYAJ2dMYl4XLZ/NvMnXwC6tbMDDEzyJ6RQKhUKhUIROqBach4C3gCuBOmASWtj4O8BLh2Zqfz5EOwn5/GR14YRFHY/Tupp5ea/Qu9JTvPHL3V/SI9bjkzOvcGHr9t27PDWRUiPbT+KnUCgUCoUiMKFGUQ0CnnL74TgBk5SyFLgRLcPxPxgn9sYlOO0Ffj06fQJhkUdSFVlMQZ2n//HVj7OxcmO7o3ZUgkGhUCgUCkVwQlVwvOsNlEKrP2w9kH5QZ/QXw9m8GWfzSuz1nmLrn4zb7yujc/DU2qd82joqsXBOzjkHb5IKhUKhUPzDCFXBWQ2McG8vAu4RQswCngTWBzvor8yO7YeFJBco701tlJ2XR9xIQ1gN83OeCXicXgSo1umFQRey/7dCoVAoFIo2hPoUvRVocSK5Da16+P+AbcB5h2Befzi1df7FG0NFCrAZrLwxfE5QmTt+uqPdMfrE9/nN51coFAqF4p9OSBYcKeVKKeUP7u1yKeWxUsoYKeVwKeWGQzvFP4gA/sSmyPZz2QTihhE3+Oyf4AicnfjDomJiTbEAzM6b3Vo1XKFQKBQKRefpVKEjIcRwIcQZQohI936kEOJvupbir2D0Pmxsp0ept9f77Gdayn32E5xOAGKdLmqbawEY1WVUp8+jUCgUCoXCQ0gKjhAiVQjxM/Ar8DbQ4iH7KPDIIZrbnw4pXR3KnDznHp/9MJ3HYjPI2sx6k8mn//BGrZBnpMsztt1lP5BpKhQKhULxjydUC85jaNFTiUCjV/sHwNEHe1J/BqT0teAcPXlyaxXw9ogMj/LZz47JZnbebADeKC7lhspqn/45FVV83u0MoufU8PZxbzMybSQj00Ye4OwVCoVCofhnE6qCMxm4VUpZ3aZ9J1rphpAQQiQIIeYJIRqEEAVCiLM6kDcKIbYIIQq92sYJIerb/EghxKnu/nOFEM42/UeEOse2hNlsdN+9mxHDhqHTdazgGIweC83bx73N5G6TuWzwZWyYtQFd93F0czha+03CgPGOWrofcRsAA5MH8uIxLyr/G4VCoVAoDpBQ/Wci8M2F00IyYO3E+Z52j5MKDAa+EEKsk1JuCiJ/PVCOJ4ILKeVSoNVM4lZePge+9jruJynl4Z2YVwA0JSOiqYmRv/yKITzcx4KT0vsW9GIexdu2+BxlMBrZMCuI33V0mo9nz8dHvnBgU1QoFAqFQhGQUC04S4BzvfalEEKPlsl4QSgDuB2TTwVul1LWSymXAZ8BATPaCSGygbOB/3Yw9CzgQyllQyjzCJWWslPSrZIInQ6h83xc59wzhthk/1Byg9Ho19ZKswWAN/aX8GlJNVkpgw7ehBUKhUKhULQSqoJzA3ChEOI7wITmWLwZGAvcHOIYfQCHlHKbV9s6IDeI/P+AW4CmYAO6labpwGttuoYIISqEENuEELcHi/QSQlwkhFgphFgZoBcAvTvKCWDUKWf4SBx14WV+R3kvUfnRbIGkvgy+fCM9biwEQzvKkEKhUCgUit9MqHlwNgMDgR+Bb4FwNAfjIVLKnSGeKwqtUKc3tXgtP7UghDgZ0Esp53Uw5ilABbDYq20JMABIQbMYnYm21OWHlPJ5dy6f4X597t+D1nsSNUdEx/jIGCPMfmO2a8EpWK79jlZFNBUKhUKhOJSEnMNGSlkCBE/N2zH1QEybthjA4t3gtso8CBwXwpizgNfdRUBb5rnLq3+DEOIuNAWno6UuX9xRVKmlpaEfI0TwSCurW7eryO/UNBQKhUKhUHSedhUcIURIEVJSyr0hiG0DDEKI3lLK7e62PKCtg3FvoDuw1B1NZARihRAlwCgp5R733DKBI4CLO5oegbL2hYhOBkhpHAQhROAIqOJ1sObN3zoFhUKhUCgUnaQjC84eAhYtaEW4+zuMn5ZSNgghPgbuEkL8Gy2K6kRgTBvRjUCm1/4Y4ClgKFpEVQvnAD+2XSITQhwLrJZSlgoh+gG3oy2ndQoZok404sTprPj0Q+0YV5BEgHPHd/b0CoVCoVAoDoCOFJwRXtsCzdflLKAwsHiHXAq8DJQBlcAlUspNQohxwFdSyigppQMoaT2pEFWAy71E5s1M4KEA55gMvCqEiEJLTvgmcF+nZxqi4Wb8Wee2KjiBx2kz0PALOj0VhUKhUCgUnaNdBUdKucp7XwjhAja08XMJGSllFXBSgHaf3DZt+hYBGQHa+wWRvw647rfMz5fgFhyd3vdji0vtQk1pcWBhm28tKibffqATUygUCoVC0QGdKrapgPOfeJ6Ln33Vp+20/2vHQNTW98ZdMVyhUCgUCsWh429aCfxgEHiNKj4t3a/NZPYPF29l7du++zqlUyoUCoVCcaj5LU/b0MOK/sp0Iu4qLDy8nXGUQqNQKBQKxe9NR2Hin7VpCgdeEEJ4VxRHSnnCwZ7YH41oT4+TErzCwVuKcCZldtMaqnaBMQqiUqB47aGcpkKhUCgUigB0tERV2WZfJXOpLYTHcuHkuZA3o7X53EeeITIuQdt5cggg4I6aP2aOCoVCoVD8w+koiuq832sifzpEEAtO+Vbt9/r3fRScxIy2OREDHD905sGZm0KhUCgUinZRTsZBsDUHcxx2L03JIEn9vPFO/HdH7QHPSaFQKBQKRWgoD9ggSBkkOXOL03AoCk69u47VUXcdnEkpFAqFQqEICaXgdBZHs/Y7mILjcnq2WwprhrUTRq5QKBQKheKgoxScdkgJFP79zhnab29Fxht7k2e7wl1T1Bh5cCemUCgUCoWiXZSC0w6psVrW4cSLLvLvtAbxqfFWcL50V4xQFhyFQqFQKH5XlJNxO4R17UqX+/9L7HHH+Xc2lAU+yN7o36YsOAqFQqFQ/K4oBacD4k7yqw3aPt4WnBaUBUehUCgUit8VtUQVCk4HNFSAw+Zpa3E2bkv1Hv82ZcFRKBQKheJ3RSk4ofD5lfBQT1jzhqfN21Kz9UtY9IBWwqHFCdkbg+nQz1GhUCgUCkUraokqFDZ9rP3+4hpPW9dh2u9mC7x7prZdu9fTrzeBs8XK04nKnQqFQqFQKA4YZcEJhUCOw/t+1n57L1Wt8SrVNf46z3Z02qGZl0KhUCgUioAoBedACZYPZ8nDnu2IuN9nLgqFQqFQKACl4IRGTEbwPqctSHsQJ2SFQqFQKBSHnN9VwRFCJAgh5gkhGoQQBUKIszqQNwohtgghCtu0S/cY9e6fF736hBDiASFEpfvnASHEb3KCGThwoLZRVxhYwOUKruBMnqP9Thv4W06tUCgUCoXiAPi9nYyfBmxAKjAY+EIIsU5KuSmI/PVAORAdoC9PSrkjQPtFwElAHiCB74DdwHOdnWx2dnbgjj5TYNvX8NEFMOHGwDKHXw2pA6D3UZ09rUKhUCj+AdjtdgoLC7FarX/0VA4Z4eHhZGRkEBYW9ruf+3dTcIQQkcCpwAApZT2wTAjxGXAOcFMA+WzgbOAa4IVOnGoW8IiUstA9ziPAhYSg4DQ1RRMRYen4DEm9NQVn08eaIhMIIaDP0Z2YtkKhUCj+SRQWFhIdHU337t35jQtiI+uVAAAW2UlEQVQNf2qklFRWVlJYWBjcYHAI+T2XqPoADinlNq+2dUBuEPn/AbcAAVIDA7BECFEihPhYCNHdqz3XPW6H5xBCXCSEWCmEWAnQ3BxixuGVr3i2A2UujkwJbRyFQqFQ/GOxWq0kJib+LZUbACEEiYmJf5iF6vdUcKKAujZttQRYfhJCnAzopZTzgow1AegO9AP2A/OFEC3WqCj3uN7niArkhyOlfF5KOVxKObwzF4LL4dlecJd/f3hsp4ZTKBQKxT+Tv6ty08IfeX2/pw9OPRDTpi0G8FkTci9lPQgEqHCpIaVc4t60CSGuRFOccoANAc4TA9RLKWWHM5Qh/iGEl15YsMy//8SnQxtHoVAoFArFIeH3tOBsAwxCiN5ebXn/3969h1lZFXoc//5GBrlligbCUKIdBANt7ODJU2KcB4eQLCMPalRmx4Jumpld9JHj5ZDh7eSlKycNwszs5KWTnqSTmODlEHVAQw3EJFEZkIvCDKLIOn+sNcM7e/bsPSOzZw97fp/nWc/svda73ne9a78ze81613oXkDvAeASxd2aRpHXA7cCQdDtqeBv7Dux+XPCKtN9Cx2hzJ63kuwVVVWSw1Nve3Z7DmZmZldXw4cM58sgjqa2tZezYeDPjF7/4BaNHj6aqqoqlS5c2b/vggw9y1FFHMXbsWFatWgXAli1bmDhxIrt27SpL+QvpsgZOCKGB2Fi5TFJ/Se8FTgbm52z6Z+CtxFlWtcCngfr0+llJoyXVStpH0gDgGuA54ImU/yfAeZJqJA0FvgLMbV8h8/TgbN/SOs6Dh83MrEIsXLiQZcuWNTdmxowZw+23387xxx/fYrtrrrmGe+65h2uvvZYf/CDO25k1axYXXnghVVXd77F6XV2izwN9gfXAz4DPhRBWSBonaRtACGFnCGFdUwA2AbvS+9eJU8x/Trwt9TSxt+ekEMJr6Rg/BP6LeLvqz8DdKa6okHfNqDz9Oide2TpusJ93Y2Zmb5ykNsOcOXOat5szZ07BbffUEUccwciRI1vFV1dX09jYSGNjI9XV1axevZpnn32W8ePH7/ExS6FLn4MTQthEfEZNbvwi4uDgfHnuB4Zl3t8HtK753ekB+FoKHSxgngsjd+jOmXdDv4GttztrAVw+pMOHNDMzKxdJTJw4EUnMmDGD6dOnt7ntBRdcwBlnnEHfvn2ZP38+559/PrNmzerC0naMVxPPyDZl+q75S3yxJKfzZ0ht/sy9+8FHb42ri5uZmXVQe+bCAEyfPr1gQ6QjFi9eTE1NDevXr6euro5Ro0a1ujXVpLa2lkceiQtNP/DAAwwZMoQQAqeddhrV1dVcc801DB48uFPK1Rm6302zstrdg9OrMTVUHryu5Sa99m07+8gT4ahTS1AuMzOzzldTUwPAoEGDmDJlCkuWLCmaJ4TArFmzmDlzJpdeeilXXnkln/nMZ7j++utLXdwOcQMnY+vLBxXfaJ80g+pf7i1tYczMzEqooaGBrVu3Nr9esGABY8aMKZrvJz/5CZMnT2bgwIE0NjZSVVVFVVUVjY2NpS5yh/gWVcamTTUMP3QZoT2z3d52bMnLY2ZmVir19fVMmTIFgJ07dzJt2jQmTZrEHXfcwdlnn82GDRv4wAc+QG1tLffeG/+pb2xsZO7cuSxYsACA8847j8mTJ9O7d29uueWWsp1LPm7gmJmZ9UCHHXYYy5cvbxU/ZcqU5oZPrn79+rFw4cLm9+PGjeOxxx4rWRn3hG9R5dXBaXand69Wq5mZWU/nBk5G03NwXnu1d8cy9s0zbdzMzMzKxreosoJYufJYtq3tSxXrWqYdPgn+/sz8+d7qpRnMzMy6E/fg5KhfN4IdDX1bJ9R+LE4Dz6cbPqLazMysJ/M3c3tV+JL2ZmZmlcS3qDJCvqUamvTK06szaTY8s7h0BTIzM7M3xD04+eR7XPaheR5dfezn4PSflr48ZmZmJTB8+HCOPPJIamtrGTt2LACbNm2irq6OESNGUFdXx+bNmwH45S9/yejRoxk3bhwbN24EYPXq1Zx22mllK38hbuC0V68OzqwyMzPbCyxcuJBly5axdOlSAGbPns2ECRNYtWoVEyZMYPbs2QDccMMN/OEPf2DGjBnND/W76KKLuu2Cm75Flcfh734Pk99/abmLYWZmPcS5557LsmXLOnWftbW1XHvttR3Od9ddd3H//fcD8MlPfpLx48dzxRVXUFVVxY4dO2hsbKS6uppFixZx8MEHM2LEiE4td2dxAyeP6n37MGDggeUuhpmZWUlJYuLEiUhixowZTJ8+nfr6eoYMGQLAwQcfTH19PQAXXHABJ5xwAkOHDuXmm29m6tSp3HrrreUsfkFu4OTRYsJUdT845qyylcXMzCrfG+lp6QyLFy+mpqaG9evXU1dXx6hRo1qkS0LpS7Guro66ujpg94KbK1eu5Oqrr+aAAw7guuuuo1+/fl1+Dm3xGJx8drwM//4O2LoOdu0E7VPuEpmZmXW6mpoaAAYNGsSUKVNYsmQJgwcP5oUXXgDghRdeYNCgQS3yNC24+YUvfIGLL76YefPmcdxxx/HTn3avSTdu4OSz4Ul4+Tl4/Few63WocgPHzMwqS0NDA1u3bm1+vWDBAsaMGcOHPvQh5s2bB8C8efM4+eSTW+S76qqrOOecc6iurmb79u1IoqqqisbGxi4/h0J8iyqf7Czx8Lp7cMzMrOLU19c3rxq+c+dOpk2bxqRJkzjmmGM49dRTufHGGznkkEO47bbbmvM8//zzLFmyhIsvvhiAs88+m2OOOYb999+fO++8syzn0RY3cPJKLZywK/50D46ZmVWYww47jOXLl7eKP/DAA/nd736XN8/QoUO5++67m99PnTqVqVOnlqyMe6JLb1FJGijpDkkNktZImlZk+96SnpC0NhN3uKS7JG2QtEnSvZJGZtLPlPS6pG2ZML6dJWz5dtfOFO0GjpmZ2d6kq8fgfBd4FRgMfAz4vqTRBbb/KrAhJ25/4FfAyLSfJcBdOds8HEIYkAn3d6yYqaHT1MDxYppmZmZ7lS775pbUHzgFmBlC2BZCWExsqHyije0PBT4OfCsbH0JYEkK4MYSwKYTwGvBtYKSkTntwTfNKDc0NHN/JMzOzzhfyLQ1UQcp5fl3ZNXE4sDOEsDITtxxoqwfnBuBCYHuR/R4PrAshbMzEHS3pRUkrJc2UlLeFImm6pKWSlsLuhk3zx7Hr9bShb1GZmVnn6tOnDxs3bqzYRk4IgY0bN9KnT5+yHL8ruyYGAC/nxL0EvCl3Q0lTgH1CCHcUGj8jaRjxttd5megHgDHAGmLj6efATnJ6ggBCCHOAOQB9Rx7e+gpr7sFxA8fMzDrXsGHDWLt2LRs25I7EqBx9+vRh2LBhZTl2VzZwtgH75cTtB2zNRqRbWVcCkwvtTNJbgAXA90IIP2uKDyE8ndnsMUmXEcfytGrgtOWdA1+BzcQp4uAeHDMz63TV1dUceuih5S5GxerKBs5KoJekESGEVSnuncCKnO1GAMOBRenx0L2BN0taBxwbQnhG0gHExs2vQgjfLHLcQKvpUYUduG9q2HiQsZmZ2V6py765QwgNwO3AZZL6S3ovcDIwP2fTPwNvBWpT+DRQn14/K2k/4F7gwRDCN3KPI+lESYPT61HATFrPsipsW1xYjMdTNvfgmJmZ7VW6umvi80BfYD3wM+BzIYQVksZJ2gYQQtgZQljXFIBNwK70/nVgCnAM8KmcZ928LR1jAvCopAbgHmKj6vKOFDL87eH4YvMz8afH4JiZme1VVKmjtztK0lbgL+UuRw91EPBiuQvRg7n+y8d1Xz6u+/IaGUJoNcmoM/kBL7v9JYQwttyF6IkkLXXdl4/rv3xc9+Xjui+vpsezlJJHz5qZmVnFcQPHzMzMKo4bOLvNKXcBejDXfXm5/svHdV8+rvvyKnn9e5CxmZmZVRz34JiZmVnFcQPHzMzMKo4bOGZmZlZxenwDR9JASXdIapC0RtK0cpdpbyVpX0k3pnrcKmmZpBMz6RMkPSmpUdJCSYfk5L1J0suS1kk6L2ffbea1liSNkPSKpJszcdPS59Ig6U5JAzNpBX8HCuW1liSdLumJVFerJY1L8b72S0jScEn3SNqc6vA7knqltFpJf0z190dJtZl8knSFpI0pXKG0CGKxvD2VpC9KWipph6S5OWkluc6L5W1TCKFHB+KSET8HBgDHAS8Bo8tdrr0xAP2BS4iLpVYBJxFXix9OfGroS8BUoA9wFfBIJu+3gEXAAcARwDpgUkormNeh1eewINXlzen96PQ5HJ+u81uAWzPbt/k7UCyvQ4t6rwPWAMem678mBV/7pa/7e4C5qY4OBh4DziEu1rwG+DKwb4pbA/RO+WYQn2A/LH1WjwOfTWkF8/bUAHwE+DDwfWBuJr5k13mhvAXLWu7KKvMH1R94FTg8EzcfmF3uslVKAB4FTgGmAw/l1P12YFR6/zwwMZP+b01fpMXyOrSo79OB24gNzaYGzuXALZlt3p6u+zcV+x0olLfc59rdAvAQcFaeeF/7pa/7J4DJmfdXAT8EJgLPkWYMp7S/Zb5YHwKmZ9LOavpiLZa3pwdgFi0bOCW7zgvlLRR6+i2qw4GdIYSVmbjlxP9abQ8prup+OLCCWKfLm9JCXF1+NTBa0gHAkGw6LT+HNvOWsvx7G0n7AZcBud23ufW3mtSoofjvQKG8lkjaBxgLvEXSU5LWptskffG13xWuBU6X1E9SDXAi8BtiPT0a0rdi8iht1C+t675QXmupJNd5O/K2qac3cAYAL+fEvUT8z9b2gKRq4KfAvBDCk8S6filns6a6HpB5n5tGkby2278BN4YQ1ubEF6v7Qr8Drvv2GQxUA/8MjANqgaOBi/C13xUeIH7hvQysBZYCd1K8/nLTXwIGpHE4rvuOKdV1Xixvm3p6A2cbsF9O3H7EMQf2BkmqIt7meBX4YoouVNfbMu9z04rlNeJgSOAE4Nt5kovVfaG6dd23z/b084YQwgshhBeBfwcm42u/pNLfm98AtxNvbRxEHKtxBR2/vvcDtqVeG9d9x5TqOi+Wt009vYGzEuglaUQm7p3EWyr2BqT/fG4k/kd7SgjhtZS0gli3Tdv1J47nWBFC2Ay8kE2n5efQZt4SncbeaDxxMPffJK0DzgdOkfQnWtffYcRBkysp/jtQKK8l6RpeC2RvZzS99rVfWgOBtwHfCSHsCCFsBH5MbFyuAI7KzowCjqKN+qV13RfKay2V5DpvR962lXugUrkDcCtxFkl/4L14FtWe1ucPgEeAATnxb0l1ewpxlPwVtBwlPxv4PfE/r1Hpgp7UnrwOAaAfcfZIU7ga+M9Ud01d9+PSdX4zLWdRtfk7UCyvQ4vP4DLgD8CgdB0vIt429LVf+rp/GvgG0AvYH7iDOOOvaSbUl4gN8y/SchbVZ4kDlGuAocQvzdxZVHnz9tSQ6rgPcWbT/PS6Vymv80J5C5a13JVV7kBs/d8JNBBHyE8rd5n21gAcQvyv9RVit2JT+FhKPwF4ktidfz8wPJN3X+Cm9GVaD5yXs+828zrk/SwuIc2iSu+npeu7AbgLGJhJK/g7UCivQ4t6qga+B2whTmO9HuiT0nztl7bua1PdbAZeJM4kHJzSjgb+mOrvT8DRmXwCrgQ2pXAlLWdNtZm3p4b0tyXkhEtSWkmu82J52wpebNPMzMwqTk8fg2NmZmYVyA0cMzMzqzhu4JiZmVnFcQPHzMzMKo4bOGZmZlZx3MAxMzOziuMGjpk1kzRX0q/LXY4sSSdLWiVpp6S5JTpGtztvM9szbuCYdRPpSzZImpkTPz7FH1SuspXZjcAviQ+S/FKJjvEl4ON7sgNJZ0raVnxLM+sKbuCYdS+vAF+V9JZyF6QzpdXl30i+/YEDgXtDCM+FEHJXHO4UIYSXQghbSrFvMysPN3DMupeFwDPAzLY2yNejI2l4ihubs82Jkv4oabukRZKGSXqfpOWStkn6taQD8xzjIkn1aZsfS+qbSZOkr0lanfb7mKSP5ynLRyXdJ2k7MKONczlA0jxJm9O+/kfS6KZzID56H+C+tM/xbeynt6TLJa2RtEPS05LOyaQfL+l/Jb2Szuvbknpn0lvcopJ0v6TvpX2+KGm9pKvTytV5PxPiAo/9UzmDpEuKnWNKf7Ok+ekYr6Syn5tJnyFpZUp7UdK9knpl0j8l6fGUvlLSl7PlLJbfrFK5gWPWvewiLhr4WUlv74T9XQqcC7ybuFDdz4F/BaYTVyAfTVxbJut9xNV6JxAXv5tIXPyuySzgLOALwDuIi+79UNIHcvbzLeLaTO8grnWVz9xUtpOBfwAagd+kBtVDqXykcgxJcfnMA84AzgOOSOXbAiCpBvhv4P+IawudBXw0la+QjwE7gfcQF1o8FzitjW0fSumNqZxDiAueFjtHiPV5JHASMBL4F+C5VPaxwHeJn+NI4mfym6aDSvoMcDnxMz0C+ArwdeDz7clvVtHKvXCXg4NDDMQvwl+n1wtJq3YTGyIBOCjf+xQ3PMWNzdnm/Zltvpji3pWJuwT4c04ZtpBZDZ44NmUHcTXx/sTF8MbllP1a4J6csnylyPmOSNsdn4l7M3FV4U+n9welbca3Yz95VxcGvgmsAqoycWemc+qXW/fp/f3Awzn7+S3wowLlOBPY9gbO8VfATW3s8yNp2ze1kf434BM5cecCj7cnv4NDJQd3U5p1T18HHpZ01R7u59HM6/r087GcuEG5eUII2cGyDwO9gbcTV/XtQ+yByK7UW028tZa1tEjZjiD2WD3cFBFCeEnSY8Ren/Y6Ou1nYYHjPBJC2JWJW0w8p7+jZR1l5cY/T+u6KqY95/h94D8l/T2xEfVfIYTfp7TfAmuAv0q6F1gA3B5C2JrGab2V2Hv2/cwxexFXyS6Yv4PnYbbX8S0qs24ohLCEOHPoyjzJTV/UysS1NYj3texu075z4zryd6Bp2w8CtZkwmngrK6uhA/vNFYpv0ikKHee1nPcdrat2HTuE8N/EGWJXE3us7pb045S2FXgXcCqxt+YC4ElJQzNl+SwtP4sxpFt7RfKbVTQ3cMy6rwuBccCknPgN6eeQTFxtJx73SEn9M++PBV4FVgOPE2/tHBJCeConrOngcZ4g/g36x6YISfsRx6M83oH9LEv7+acCxzk2Z4Dwcew+p87yKrBPnmMXPccQwoshhPkhhDOJY4Q+KWnflLYzhHBfCOEC4CjibcKTQgj1xF6lt+f5LJ7K7Dtv/k48b7NuybeozLqpEMJTkubQ+tkvTwHPApdI+gZxzMtFnXjoXsBNki4DhgKzgf8IITQASLoauFqSgAeAAcRG0K4Qwpz2HiSEsErSXcRbLNOJY3++CbwM3NKB/ayUdBvwI0lfAv4EDAOGhxDmEwc6nwt8T9J1wGHpnL4TQmhs73Ha4Rmgj6Q64oDmxvacY6rnPwEriHX/EeDpEMIOSScRbw0+AGwiNuLeRGw4AVwM3CBpC3APsSfvXUBNCOFb7chvVrHcg2PWvV1GnMnTLN1iOp34Rb2cOEPmwk485u+JX7YLgTuA+4CvZdJnEgcnn5+2+y1xltNf38CxPgUsIQ60XQL0Iw4W3t7B/ZxBbDBcDzxJHDT8ZoAQwnPAicSxOsuAm4Cf0bl1RgjhIeAHad8b2F1nxc5xB7HRsxx4kNgA+WBK2wJ8GPifdF7nEwcnL0rH/BFx1tUnUv5FxBlyf21PfrNKphC66la3mZmZWddwD46ZmZlVHDdwzMzMrOK4gWNmZmYVxw0cMzMzqzhu4JiZmVnFcQPHzMzMKo4bOGZmZlZx3MAxMzOzivP/PV0GexdowdUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x252 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,3.5))\n",
    "plt.plot(cumulative_heads_ratio)\n",
    "plt.plot([0, 10000], [0.51, 0.51], \"k--\", linewidth=2, label=\"51%\")\n",
    "plt.plot([0, 10000], [0.5, 0.5], \"k-\", label=\"50%\")\n",
    "plt.xlabel(\"Number of coin tosses\")\n",
    "plt.ylabel(\"Heads ratio\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.axis([0, 10000, 0.42, 0.58])\n",
    "save_fig(\"law_of_large_numbers_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import make_moons\n",
    "\n",
    "X, y = make_moons(n_samples=500, noise=0.30, random_state=42)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning**: In Scikit-Learn 0.20, some hyperparameters (`solver`, `n_estimators`, `gamma`, etc.) start issuing warnings about the fact that their default value will change in Scikit-Learn 0.22. To avoid these warnings and ensure that this notebooks keeps producing the same outputs as in the book, I set the hyperparameters to their old default value.  In your own code, you can simply rely on the latest default values instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "log_clf = LogisticRegression(solver=\"liblinear\", random_state=42)\n",
    "rnd_clf = RandomForestClassifier(n_estimators=10, random_state=42)\n",
    "svm_clf = SVC(gamma=\"auto\", random_state=42)\n",
    "\n",
    "voting_clf = VotingClassifier(\n",
    "    estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n",
    "    voting='hard')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('lr', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=42, solver='liblinear',\n",
       "          tol=0.0001, verbose=0, warm_start=False)), ('rf', Rando...f',\n",
       "  max_iter=-1, probability=False, random_state=42, shrinking=True,\n",
       "  tol=0.001, verbose=False))],\n",
       "         flatten_transform=None, n_jobs=None, voting='hard', weights=None)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression 0.864\n",
      "RandomForestClassifier 0.872\n",
      "SVC 0.888\n",
      "VotingClassifier 0.896\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "for clf in (log_clf, rnd_clf, svm_clf, voting_clf):\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('lr', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=42, solver='liblinear',\n",
       "          tol=0.0001, verbose=0, warm_start=False)), ('rf', Rando...bf',\n",
       "  max_iter=-1, probability=True, random_state=42, shrinking=True,\n",
       "  tol=0.001, verbose=False))],\n",
       "         flatten_transform=None, n_jobs=None, voting='soft', weights=None)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_clf = LogisticRegression(solver=\"liblinear\", random_state=42)\n",
    "rnd_clf = RandomForestClassifier(n_estimators=10, random_state=42)\n",
    "svm_clf = SVC(gamma=\"auto\", probability=True, random_state=42)\n",
    "\n",
    "voting_clf = VotingClassifier(\n",
    "    estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n",
    "    voting='soft')\n",
    "voting_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression 0.864\n",
      "RandomForestClassifier 0.872\n",
      "SVC 0.888\n",
      "VotingClassifier 0.912\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "for clf in (log_clf, rnd_clf, svm_clf, voting_clf):\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bagging ensembles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "bag_clf = BaggingClassifier(\n",
    "    DecisionTreeClassifier(random_state=42), n_estimators=500,\n",
    "    max_samples=100, bootstrap=True, n_jobs=-1, random_state=42)\n",
    "bag_clf.fit(X_train, y_train)\n",
    "y_pred = bag_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.904\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.856\n"
     ]
    }
   ],
   "source": [
    "tree_clf = DecisionTreeClassifier(random_state=42)\n",
    "tree_clf.fit(X_train, y_train)\n",
    "y_pred_tree = tree_clf.predict(X_test)\n",
    "print(accuracy_score(y_test, y_pred_tree))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "def plot_decision_boundary(clf, X, y, axes=[-1.5, 2.5, -1, 1.5], alpha=0.5, contour=True):\n",
    "    x1s = np.linspace(axes[0], axes[1], 100)\n",
    "    x2s = np.linspace(axes[2], axes[3], 100)\n",
    "    x1, x2 = np.meshgrid(x1s, x2s)\n",
    "    X_new = np.c_[x1.ravel(), x2.ravel()]\n",
    "    y_pred = clf.predict(X_new).reshape(x1.shape)\n",
    "    custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\n",
    "    plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)\n",
    "    if contour:\n",
    "        custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])\n",
    "        plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\n",
    "    plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", alpha=alpha)\n",
    "    plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", alpha=alpha)\n",
    "    plt.axis(axes)\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=18)\n",
    "    plt.ylabel(r\"$x_2$\", fontsize=18, rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure decision_tree_without_and_with_bagging_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,4))\n",
    "plt.subplot(121)\n",
    "plot_decision_boundary(tree_clf, X, y)\n",
    "plt.title(\"Decision Tree\", fontsize=14)\n",
    "plt.subplot(122)\n",
    "plot_decision_boundary(bag_clf, X, y)\n",
    "plt.title(\"Decision Trees with Bagging\", fontsize=14)\n",
    "save_fig(\"decision_tree_without_and_with_bagging_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "bag_clf = BaggingClassifier(\n",
    "    DecisionTreeClassifier(splitter=\"random\", max_leaf_nodes=16, random_state=42),\n",
    "    n_estimators=500, max_samples=1.0, bootstrap=True, n_jobs=-1, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "bag_clf.fit(X_train, y_train)\n",
    "y_pred = bag_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16, n_jobs=-1, random_state=42)\n",
    "rnd_clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred_rf = rnd_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.976"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(y_pred == y_pred_rf) / len(y_pred)  # almost identical predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sepal length (cm) 0.11249225099876374\n",
      "sepal width (cm) 0.023119288282510326\n",
      "petal length (cm) 0.44103046436395765\n",
      "petal width (cm) 0.4233579963547681\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "rnd_clf = RandomForestClassifier(n_estimators=500, n_jobs=-1, random_state=42)\n",
    "rnd_clf.fit(iris[\"data\"], iris[\"target\"])\n",
    "for name, score in zip(iris[\"feature_names\"], rnd_clf.feature_importances_):\n",
    "    print(name, score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.11249225, 0.02311929, 0.44103046, 0.423358  ])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_clf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "\n",
    "for i in range(15):\n",
    "    tree_clf = DecisionTreeClassifier(max_leaf_nodes=16, random_state=42 + i)\n",
    "    indices_with_replacement = np.random.randint(0, len(X_train), len(X_train))\n",
    "    tree_clf.fit(X[indices_with_replacement], y[indices_with_replacement])\n",
    "    plot_decision_boundary(tree_clf, X, y, axes=[-1.5, 2.5, -1, 1.5], alpha=0.02, contour=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Out-of-Bag evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9013333333333333"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_clf = BaggingClassifier(\n",
    "    DecisionTreeClassifier(random_state=42), n_estimators=500,\n",
    "    bootstrap=True, n_jobs=-1, oob_score=True, random_state=40)\n",
    "bag_clf.fit(X_train, y_train)\n",
    "bag_clf.oob_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.31746032, 0.68253968],\n",
       "       [0.34117647, 0.65882353],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.08379888, 0.91620112],\n",
       "       [0.31693989, 0.68306011],\n",
       "       [0.02923977, 0.97076023],\n",
       "       [0.97687861, 0.02312139],\n",
       "       [0.97765363, 0.02234637],\n",
       "       [0.74404762, 0.25595238],\n",
       "       [0.        , 1.        ],\n",
       "       [0.71195652, 0.28804348],\n",
       "       [0.83957219, 0.16042781],\n",
       "       [0.97777778, 0.02222222],\n",
       "       [0.0625    , 0.9375    ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.97297297, 0.02702703],\n",
       "       [0.95238095, 0.04761905],\n",
       "       [1.        , 0.        ],\n",
       "       [0.01704545, 0.98295455],\n",
       "       [0.38947368, 0.61052632],\n",
       "       [0.88700565, 0.11299435],\n",
       "       [1.        , 0.        ],\n",
       "       [0.96685083, 0.03314917],\n",
       "       [0.        , 1.        ],\n",
       "       [0.99428571, 0.00571429],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.64804469, 0.35195531],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.13402062, 0.86597938],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.36065574, 0.63934426],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.27093596, 0.72906404],\n",
       "       [0.34146341, 0.65853659],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.00531915, 0.99468085],\n",
       "       [0.98265896, 0.01734104],\n",
       "       [0.91428571, 0.08571429],\n",
       "       [0.97282609, 0.02717391],\n",
       "       [0.97029703, 0.02970297],\n",
       "       [0.        , 1.        ],\n",
       "       [0.06134969, 0.93865031],\n",
       "       [0.98019802, 0.01980198],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.97790055, 0.02209945],\n",
       "       [0.79473684, 0.20526316],\n",
       "       [0.41919192, 0.58080808],\n",
       "       [0.99473684, 0.00526316],\n",
       "       [0.        , 1.        ],\n",
       "       [0.67613636, 0.32386364],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.87356322, 0.12643678],\n",
       "       [1.        , 0.        ],\n",
       "       [0.56140351, 0.43859649],\n",
       "       [0.16304348, 0.83695652],\n",
       "       [0.67539267, 0.32460733],\n",
       "       [0.90673575, 0.09326425],\n",
       "       [0.        , 1.        ],\n",
       "       [0.16201117, 0.83798883],\n",
       "       [0.89005236, 0.10994764],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.995     , 0.005     ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.07272727, 0.92727273],\n",
       "       [0.05418719, 0.94581281],\n",
       "       [0.29533679, 0.70466321],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.81871345, 0.18128655],\n",
       "       [0.01092896, 0.98907104],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.22513089, 0.77486911],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.9368932 , 0.0631068 ],\n",
       "       [0.76536313, 0.23463687],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.17127072, 0.82872928],\n",
       "       [0.65306122, 0.34693878],\n",
       "       [0.        , 1.        ],\n",
       "       [0.03076923, 0.96923077],\n",
       "       [0.49444444, 0.50555556],\n",
       "       [1.        , 0.        ],\n",
       "       [0.02673797, 0.97326203],\n",
       "       [0.98870056, 0.01129944],\n",
       "       [0.23121387, 0.76878613],\n",
       "       [0.5       , 0.5       ],\n",
       "       [0.9947644 , 0.0052356 ],\n",
       "       [0.00555556, 0.99444444],\n",
       "       [0.98963731, 0.01036269],\n",
       "       [0.25641026, 0.74358974],\n",
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       "       [0.0106383 , 0.9893617 ],\n",
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       "       [0.98181818, 0.01818182],\n",
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       "       [0.97752809, 0.02247191],\n",
       "       [0.99453552, 0.00546448],\n",
       "       [0.01960784, 0.98039216],\n",
       "       [0.18367347, 0.81632653],\n",
       "       [0.98387097, 0.01612903],\n",
       "       [0.29533679, 0.70466321],\n",
       "       [0.98295455, 0.01704545],\n",
       "       [0.        , 1.        ],\n",
       "       [0.00561798, 0.99438202],\n",
       "       [0.75138122, 0.24861878],\n",
       "       [0.38624339, 0.61375661],\n",
       "       [0.42708333, 0.57291667],\n",
       "       [0.86315789, 0.13684211],\n",
       "       [0.92964824, 0.07035176],\n",
       "       [0.05699482, 0.94300518],\n",
       "       [0.82802548, 0.17197452],\n",
       "       [0.01546392, 0.98453608],\n",
       "       [0.        , 1.        ],\n",
       "       [0.02298851, 0.97701149],\n",
       "       [0.96721311, 0.03278689],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.01041667, 0.98958333],\n",
       "       [0.        , 1.        ],\n",
       "       [0.0326087 , 0.9673913 ],\n",
       "       [0.01020408, 0.98979592],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.93785311, 0.06214689],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.99462366, 0.00537634],\n",
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       "       [0.38860104, 0.61139896],\n",
       "       [0.32065217, 0.67934783],\n",
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       "       [0.31182796, 0.68817204],\n",
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       "       [1.        , 0.        ],\n",
       "       [0.00588235, 0.99411765],\n",
       "       [0.        , 1.        ],\n",
       "       [0.98387097, 0.01612903],\n",
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       "       [0.        , 1.        ],\n",
       "       [0.62264151, 0.37735849],\n",
       "       [0.92344498, 0.07655502],\n",
       "       [0.        , 1.        ],\n",
       "       [0.99526066, 0.00473934],\n",
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       "       [0.98888889, 0.01111111],\n",
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       "       [0.06451613, 0.93548387],\n",
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       "       [0.05154639, 0.94845361],\n",
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       "       [0.03278689, 0.96721311],\n",
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       "       [0.95808383, 0.04191617],\n",
       "       [0.79532164, 0.20467836],\n",
       "       [0.55665025, 0.44334975],\n",
       "       [0.        , 1.        ],\n",
       "       [0.18604651, 0.81395349],\n",
       "       [1.        , 0.        ],\n",
       "       [0.93121693, 0.06878307],\n",
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       "       [0.00531915, 0.99468085],\n",
       "       [0.        , 1.        ],\n",
       "       [0.44623656, 0.55376344],\n",
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       "       [0.00558659, 0.99441341],\n",
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       "       [0.96923077, 0.03076923],\n",
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       "       [0.19767442, 0.80232558],\n",
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       "       [0.20338983, 0.79661017],\n",
       "       [0.98181818, 0.01818182],\n",
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       "       [0.98969072, 0.01030928],\n",
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       "       [0.08717949, 0.91282051],\n",
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       "       [0.82142857, 0.17857143],\n",
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       "       [0.125     , 0.875     ],\n",
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       "       [0.89150943, 0.10849057],\n",
       "       [0.1978022 , 0.8021978 ],\n",
       "       [0.95238095, 0.04761905],\n",
       "       [0.00515464, 0.99484536],\n",
       "       [0.609375  , 0.390625  ],\n",
       "       [0.07692308, 0.92307692],\n",
       "       [0.99484536, 0.00515464],\n",
       "       [0.84210526, 0.15789474],\n",
       "       [0.        , 1.        ],\n",
       "       [0.99484536, 0.00515464],\n",
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       "       [0.26903553, 0.73096447],\n",
       "       [0.98461538, 0.01538462],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.00574713, 0.99425287],\n",
       "       [0.85142857, 0.14857143],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.76506024, 0.23493976],\n",
       "       [0.8969697 , 0.1030303 ],\n",
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       "       [1.        , 0.        ],\n",
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       "       [0.74752475, 0.25247525],\n",
       "       [0.09146341, 0.90853659],\n",
       "       [0.44329897, 0.55670103],\n",
       "       [0.22395833, 0.77604167],\n",
       "       [0.        , 1.        ],\n",
       "       [0.87046632, 0.12953368],\n",
       "       [0.78212291, 0.21787709],\n",
       "       [0.00507614, 0.99492386],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.02884615, 0.97115385],\n",
       "       [0.96571429, 0.03428571],\n",
       "       [0.93478261, 0.06521739],\n",
       "       [1.        , 0.        ],\n",
       "       [0.49756098, 0.50243902],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.01604278, 0.98395722],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.96987952, 0.03012048],\n",
       "       [0.        , 1.        ],\n",
       "       [0.05747126, 0.94252874],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.98989899, 0.01010101],\n",
       "       [0.01675978, 0.98324022],\n",
       "       [1.        , 0.        ],\n",
       "       [0.13541667, 0.86458333],\n",
       "       [0.        , 1.        ],\n",
       "       [0.00546448, 0.99453552],\n",
       "       [0.        , 1.        ],\n",
       "       [0.41836735, 0.58163265],\n",
       "       [0.11309524, 0.88690476],\n",
       "       [0.22110553, 0.77889447],\n",
       "       [1.        , 0.        ],\n",
       "       [0.97647059, 0.02352941],\n",
       "       [0.22826087, 0.77173913],\n",
       "       [0.98882682, 0.01117318],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.96428571, 0.03571429],\n",
       "       [0.33507853, 0.66492147],\n",
       "       [0.98235294, 0.01764706],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.99465241, 0.00534759],\n",
       "       [0.        , 1.        ],\n",
       "       [0.06043956, 0.93956044],\n",
       "       [0.97619048, 0.02380952],\n",
       "       [1.        , 0.        ],\n",
       "       [0.03108808, 0.96891192],\n",
       "       [0.57291667, 0.42708333]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_clf.oob_decision_function_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.912"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "y_pred = bag_clf.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    from sklearn.datasets import fetch_openml\n",
    "    mnist = fetch_openml('mnist_784', version=1)\n",
    "    mnist.target = mnist.target.astype(np.int64)\n",
    "except ImportError:\n",
    "    from sklearn.datasets import fetch_mldata\n",
    "    mnist = fetch_mldata('MNIST original')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "            oob_score=False, random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_clf = RandomForestClassifier(n_estimators=10, random_state=42)\n",
    "rnd_clf.fit(mnist[\"data\"], mnist[\"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_digit(data):\n",
    "    image = data.reshape(28, 28)\n",
    "    plt.imshow(image, cmap = mpl.cm.hot,\n",
    "               interpolation=\"nearest\")\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure mnist_feature_importance_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_digit(rnd_clf.feature_importances_)\n",
    "\n",
    "cbar = plt.colorbar(ticks=[rnd_clf.feature_importances_.min(), rnd_clf.feature_importances_.max()])\n",
    "cbar.ax.set_yticklabels(['Not important', 'Very important'])\n",
    "\n",
    "save_fig(\"mnist_feature_importance_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(algorithm='SAMME.R',\n",
       "          base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best'),\n",
       "          learning_rate=0.5, n_estimators=200, random_state=42)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "ada_clf = AdaBoostClassifier(\n",
    "    DecisionTreeClassifier(max_depth=1), n_estimators=200,\n",
    "    algorithm=\"SAMME.R\", learning_rate=0.5, random_state=42)\n",
    "ada_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(ada_clf, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure boosting_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = len(X_train)\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "for subplot, learning_rate in ((121, 1), (122, 0.5)):\n",
    "    sample_weights = np.ones(m)\n",
    "    plt.subplot(subplot)\n",
    "    for i in range(5):\n",
    "        svm_clf = SVC(kernel=\"rbf\", C=0.05, gamma=\"auto\", random_state=42)\n",
    "        svm_clf.fit(X_train, y_train, sample_weight=sample_weights)\n",
    "        y_pred = svm_clf.predict(X_train)\n",
    "        sample_weights[y_pred != y_train] *= (1 + learning_rate)\n",
    "        plot_decision_boundary(svm_clf, X, y, alpha=0.2)\n",
    "        plt.title(\"learning_rate = {}\".format(learning_rate), fontsize=16)\n",
    "    if subplot == 121:\n",
    "        plt.text(-0.7, -0.65, \"1\", fontsize=14)\n",
    "        plt.text(-0.6, -0.10, \"2\", fontsize=14)\n",
    "        plt.text(-0.5,  0.10, \"3\", fontsize=14)\n",
    "        plt.text(-0.4,  0.55, \"4\", fontsize=14)\n",
    "        plt.text(-0.3,  0.90, \"5\", fontsize=14)\n",
    "\n",
    "save_fig(\"boosting_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['base_estimator_',\n",
       " 'classes_',\n",
       " 'estimator_errors_',\n",
       " 'estimator_weights_',\n",
       " 'estimators_',\n",
       " 'feature_importances_',\n",
       " 'n_classes_']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(m for m in dir(ada_clf) if not m.startswith(\"_\") and m.endswith(\"_\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "X = np.random.rand(100, 1) - 0.5\n",
    "y = 3*X[:, 0]**2 + 0.05 * np.random.randn(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=2, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=42, splitter='best')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg1 = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
    "tree_reg1.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=2, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=42, splitter='best')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2 = y - tree_reg1.predict(X)\n",
    "tree_reg2 = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
    "tree_reg2.fit(X, y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=2, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=42, splitter='best')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y3 = y2 - tree_reg2.predict(X)\n",
    "tree_reg3 = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
    "tree_reg3.fit(X, y3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_new = np.array([[0.8]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = sum(tree.predict(X_new) for tree in (tree_reg1, tree_reg2, tree_reg3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.75026781])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure gradient_boosting_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x792 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_predictions(regressors, X, y, axes, label=None, style=\"r-\", data_style=\"b.\", data_label=None):\n",
    "    x1 = np.linspace(axes[0], axes[1], 500)\n",
    "    y_pred = sum(regressor.predict(x1.reshape(-1, 1)) for regressor in regressors)\n",
    "    plt.plot(X[:, 0], y, data_style, label=data_label)\n",
    "    plt.plot(x1, y_pred, style, linewidth=2, label=label)\n",
    "    if label or data_label:\n",
    "        plt.legend(loc=\"upper center\", fontsize=16)\n",
    "    plt.axis(axes)\n",
    "\n",
    "plt.figure(figsize=(11,11))\n",
    "\n",
    "plt.subplot(321)\n",
    "plot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h_1(x_1)$\", style=\"g-\", data_label=\"Training set\")\n",
    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
    "plt.title(\"Residuals and tree predictions\", fontsize=16)\n",
    "\n",
    "plt.subplot(322)\n",
    "plot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h(x_1) = h_1(x_1)$\", data_label=\"Training set\")\n",
    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
    "plt.title(\"Ensemble predictions\", fontsize=16)\n",
    "\n",
    "plt.subplot(323)\n",
    "plot_predictions([tree_reg2], X, y2, axes=[-0.5, 0.5, -0.5, 0.5], label=\"$h_2(x_1)$\", style=\"g-\", data_style=\"k+\", data_label=\"Residuals\")\n",
    "plt.ylabel(\"$y - h_1(x_1)$\", fontsize=16)\n",
    "\n",
    "plt.subplot(324)\n",
    "plot_predictions([tree_reg1, tree_reg2], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h(x_1) = h_1(x_1) + h_2(x_1)$\")\n",
    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
    "\n",
    "plt.subplot(325)\n",
    "plot_predictions([tree_reg3], X, y3, axes=[-0.5, 0.5, -0.5, 0.5], label=\"$h_3(x_1)$\", style=\"g-\", data_style=\"k+\")\n",
    "plt.ylabel(\"$y - h_1(x_1) - h_2(x_1)$\", fontsize=16)\n",
    "plt.xlabel(\"$x_1$\", fontsize=16)\n",
    "\n",
    "plt.subplot(326)\n",
    "plot_predictions([tree_reg1, tree_reg2, tree_reg3], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h(x_1) = h_1(x_1) + h_2(x_1) + h_3(x_1)$\")\n",
    "plt.xlabel(\"$x_1$\", fontsize=16)\n",
    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
    "\n",
    "save_fig(\"gradient_boosting_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=1.0, loss='ls', max_depth=2, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=1,\n",
       "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=3, n_iter_no_change=None, presort='auto',\n",
       "             random_state=42, subsample=1.0, tol=0.0001,\n",
       "             validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "gbrt = GradientBoostingRegressor(max_depth=2, n_estimators=3, learning_rate=1.0, random_state=42)\n",
    "gbrt.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.1, loss='ls', max_depth=2, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=1,\n",
       "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=200, n_iter_no_change=None, presort='auto',\n",
       "             random_state=42, subsample=1.0, tol=0.0001,\n",
       "             validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbrt_slow = GradientBoostingRegressor(max_depth=2, n_estimators=200, learning_rate=0.1, random_state=42)\n",
    "gbrt_slow.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure gbrt_learning_rate_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plot_predictions([gbrt], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"Ensemble predictions\")\n",
    "plt.title(\"learning_rate={}, n_estimators={}\".format(gbrt.learning_rate, gbrt.n_estimators), fontsize=14)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_predictions([gbrt_slow], X, y, axes=[-0.5, 0.5, -0.1, 0.8])\n",
    "plt.title(\"learning_rate={}, n_estimators={}\".format(gbrt_slow.learning_rate, gbrt_slow.n_estimators), fontsize=14)\n",
    "\n",
    "save_fig(\"gbrt_learning_rate_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Boosting with Early stopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.1, loss='ls', max_depth=2, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=1,\n",
       "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=55, n_iter_no_change=None, presort='auto',\n",
       "             random_state=42, subsample=1.0, tol=0.0001,\n",
       "             validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, random_state=49)\n",
    "\n",
    "gbrt = GradientBoostingRegressor(max_depth=2, n_estimators=120, random_state=42)\n",
    "gbrt.fit(X_train, y_train)\n",
    "\n",
    "errors = [mean_squared_error(y_val, y_pred)\n",
    "          for y_pred in gbrt.staged_predict(X_val)]\n",
    "bst_n_estimators = np.argmin(errors) + 1\n",
    "\n",
    "gbrt_best = GradientBoostingRegressor(max_depth=2,n_estimators=bst_n_estimators, random_state=42)\n",
    "gbrt_best.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_error = np.min(errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure early_stopping_gbrt_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(errors, \"b.-\")\n",
    "plt.plot([bst_n_estimators, bst_n_estimators], [0, min_error], \"k--\")\n",
    "plt.plot([0, 120], [min_error, min_error], \"k--\")\n",
    "plt.plot(bst_n_estimators, min_error, \"ko\")\n",
    "plt.text(bst_n_estimators, min_error*1.2, \"Minimum\", ha=\"center\", fontsize=14)\n",
    "plt.axis([0, 120, 0, 0.01])\n",
    "plt.xlabel(\"Number of trees\")\n",
    "plt.title(\"Validation error\", fontsize=14)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_predictions([gbrt_best], X, y, axes=[-0.5, 0.5, -0.1, 0.8])\n",
    "plt.title(\"Best model (%d trees)\" % bst_n_estimators, fontsize=14)\n",
    "\n",
    "save_fig(\"early_stopping_gbrt_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbrt = GradientBoostingRegressor(max_depth=2, warm_start=True, random_state=42)\n",
    "\n",
    "min_val_error = float(\"inf\")\n",
    "error_going_up = 0\n",
    "for n_estimators in range(1, 120):\n",
    "    gbrt.n_estimators = n_estimators\n",
    "    gbrt.fit(X_train, y_train)\n",
    "    y_pred = gbrt.predict(X_val)\n",
    "    val_error = mean_squared_error(y_val, y_pred)\n",
    "    if val_error < min_val_error:\n",
    "        min_val_error = val_error\n",
    "        error_going_up = 0\n",
    "    else:\n",
    "        error_going_up += 1\n",
    "        if error_going_up == 5:\n",
    "            break  # early stopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61\n"
     ]
    }
   ],
   "source": [
    "print(gbrt.n_estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum validation MSE: 0.002712853325235463\n"
     ]
    }
   ],
   "source": [
    "print(\"Minimum validation MSE:\", min_val_error)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import xgboost\n",
    "except ImportError as ex:\n",
    "    print(\"Error: the xgboost library is not installed.\")\n",
    "    xgboost = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation MSE: 0.0028512559726563943\n"
     ]
    }
   ],
   "source": [
    "if xgboost is not None:  # not shown in the book\n",
    "    xgb_reg = xgboost.XGBRegressor(random_state=42)\n",
    "    xgb_reg.fit(X_train, y_train)\n",
    "    y_pred = xgb_reg.predict(X_val)\n",
    "    val_error = mean_squared_error(y_val, y_pred)\n",
    "    print(\"Validation MSE:\", val_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-rmse:0.286719\n",
      "Will train until validation_0-rmse hasn't improved in 2 rounds.\n",
      "[1]\tvalidation_0-rmse:0.258221\n",
      "[2]\tvalidation_0-rmse:0.232634\n",
      "[3]\tvalidation_0-rmse:0.210526\n",
      "[4]\tvalidation_0-rmse:0.190232\n",
      "[5]\tvalidation_0-rmse:0.172196\n",
      "[6]\tvalidation_0-rmse:0.156394\n",
      "[7]\tvalidation_0-rmse:0.142241\n",
      "[8]\tvalidation_0-rmse:0.129789\n",
      "[9]\tvalidation_0-rmse:0.118752\n",
      "[10]\tvalidation_0-rmse:0.108388\n",
      "[11]\tvalidation_0-rmse:0.100155\n",
      "[12]\tvalidation_0-rmse:0.09208\n",
      "[13]\tvalidation_0-rmse:0.084791\n",
      "[14]\tvalidation_0-rmse:0.078699\n",
      "[15]\tvalidation_0-rmse:0.073248\n",
      "[16]\tvalidation_0-rmse:0.069391\n",
      "[17]\tvalidation_0-rmse:0.066277\n",
      "[18]\tvalidation_0-rmse:0.063458\n",
      "[19]\tvalidation_0-rmse:0.060326\n",
      "[20]\tvalidation_0-rmse:0.0578\n",
      "[21]\tvalidation_0-rmse:0.055643\n",
      "[22]\tvalidation_0-rmse:0.053943\n",
      "[23]\tvalidation_0-rmse:0.053138\n",
      "[24]\tvalidation_0-rmse:0.052415\n",
      "[25]\tvalidation_0-rmse:0.051821\n",
      "[26]\tvalidation_0-rmse:0.051226\n",
      "[27]\tvalidation_0-rmse:0.051135\n",
      "[28]\tvalidation_0-rmse:0.05091\n",
      "[29]\tvalidation_0-rmse:0.050893\n",
      "[30]\tvalidation_0-rmse:0.050725\n",
      "[31]\tvalidation_0-rmse:0.050471\n",
      "[32]\tvalidation_0-rmse:0.050285\n",
      "[33]\tvalidation_0-rmse:0.050492\n",
      "[34]\tvalidation_0-rmse:0.050348\n",
      "Stopping. Best iteration:\n",
      "[32]\tvalidation_0-rmse:0.050285\n",
      "\n",
      "Validation MSE: 0.002528626115371327\n"
     ]
    }
   ],
   "source": [
    "if xgboost is not None:  # not shown in the book\n",
    "    xgb_reg.fit(X_train, y_train,\n",
    "                eval_set=[(X_val, y_val)], early_stopping_rounds=2)\n",
    "    y_pred = xgb_reg.predict(X_val)\n",
    "    val_error = mean_squared_error(y_val, y_pred)\n",
    "    print(\"Validation MSE:\", val_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.22 ms ± 119 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit xgboost.XGBRegressor().fit(X_train, y_train) if xgboost is not None else None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.8 ms ± 421 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit GradientBoostingRegressor().fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Exercise solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. to 7."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See Appendix A."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Voting Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise: _Load the MNIST data and split it into a training set, a validation set, and a test set (e.g., use 50,000 instances for training, 10,000 for validation, and 10,000 for testing)._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MNIST dataset was loaded earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_val, X_test, y_train_val, y_test = train_test_split(\n",
    "    mnist.data, mnist.target, test_size=10000, random_state=42)\n",
    "X_train, X_val, y_train, y_val = train_test_split(\n",
    "    X_train_val, y_train_val, test_size=10000, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise: _Then train various classifiers, such as a Random Forest classifier, an Extra-Trees classifier, and an SVM._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_forest_clf = RandomForestClassifier(n_estimators=10, random_state=42)\n",
    "extra_trees_clf = ExtraTreesClassifier(n_estimators=10, random_state=42)\n",
    "svm_clf = LinearSVC(random_state=42)\n",
    "mlp_clf = MLPClassifier(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training the RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
      "            oob_score=False, random_state=42, verbose=0, warm_start=False)\n",
      "Training the ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',\n",
      "           max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "           min_samples_leaf=1, min_samples_split=2,\n",
      "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
      "           oob_score=False, random_state=42, verbose=0, warm_start=False)\n",
      "Training the LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n",
      "     verbose=0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ageron/.virtualenvs/ml/lib/python3.6/site-packages/sklearn/svm/base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training the MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
      "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
      "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
      "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
      "       n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n",
      "       random_state=42, shuffle=True, solver='adam', tol=0.0001,\n",
      "       validation_fraction=0.1, verbose=False, warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "estimators = [random_forest_clf, extra_trees_clf, svm_clf, mlp_clf]\n",
    "for estimator in estimators:\n",
    "    print(\"Training the\", estimator)\n",
    "    estimator.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9469, 0.9492, 0.8641, 0.9629]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[estimator.score(X_val, y_val) for estimator in estimators]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The linear SVM is far outperformed by the other classifiers. However, let's keep it for now since it may improve the voting classifier's performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise: _Next, try to combine them into an ensemble that outperforms them all on the validation set, using a soft or hard voting classifier._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import VotingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "named_estimators = [\n",
    "    (\"random_forest_clf\", random_forest_clf),\n",
    "    (\"extra_trees_clf\", extra_trees_clf),\n",
    "    (\"svm_clf\", svm_clf),\n",
    "    (\"mlp_clf\", mlp_clf),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "voting_clf = VotingClassifier(named_estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ageron/.virtualenvs/ml/lib/python3.6/site-packages/sklearn/svm/base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('random_forest_clf', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "   ...=True, solver='adam', tol=0.0001,\n",
       "       validation_fraction=0.1, verbose=False, warm_start=False))],\n",
       "         flatten_transform=None, n_jobs=None, voting='hard', weights=None)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9616"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.score(X_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9469, 0.9492, 0.8641, 0.9629]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[estimator.score(X_val, y_val) for estimator in voting_clf.estimators_]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's remove the SVM to see if performance improves. It is possible to remove an estimator by setting it to `None` using `set_params()` like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('random_forest_clf', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "   ...=True, solver='adam', tol=0.0001,\n",
       "       validation_fraction=0.1, verbose=False, warm_start=False))],\n",
       "         flatten_transform=None, n_jobs=None, voting='hard', weights=None)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.set_params(svm_clf=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This updated the list of estimators:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('random_forest_clf',\n",
       "  RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "              max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "              min_samples_leaf=1, min_samples_split=2,\n",
       "              min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "              oob_score=False, random_state=42, verbose=0, warm_start=False)),\n",
       " ('extra_trees_clf',\n",
       "  ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',\n",
       "             max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "             min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "             min_samples_leaf=1, min_samples_split=2,\n",
       "             min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "             oob_score=False, random_state=42, verbose=0, warm_start=False)),\n",
       " ('svm_clf', None),\n",
       " ('mlp_clf',\n",
       "  MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "         beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "         hidden_layer_sizes=(100,), learning_rate='constant',\n",
       "         learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "         n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n",
       "         random_state=42, shuffle=True, solver='adam', tol=0.0001,\n",
       "         validation_fraction=0.1, verbose=False, warm_start=False))]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.estimators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, it did not update the list of _trained_ estimators:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "             max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "             min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "             min_samples_leaf=1, min_samples_split=2,\n",
       "             min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "             oob_score=False, random_state=42, verbose=0, warm_start=False),\n",
       " ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "            oob_score=False, random_state=42, verbose=0, warm_start=False),\n",
       " LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "      intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "      multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n",
       "      verbose=0),\n",
       " MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "        beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "        hidden_layer_sizes=(100,), learning_rate='constant',\n",
       "        learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "        n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n",
       "        random_state=42, shuffle=True, solver='adam', tol=0.0001,\n",
       "        validation_fraction=0.1, verbose=False, warm_start=False)]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.estimators_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we can either fit the `VotingClassifier` again, or just remove the SVM from the list of trained estimators:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "del voting_clf.estimators_[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's evaluate the `VotingClassifier` again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9648"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.score(X_val, y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A bit better! The SVM was hurting performance. Now let's try using a soft voting classifier. We do not actually need to retrain the classifier, we can just set `voting` to `\"soft\"`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "voting_clf.voting = \"soft\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9703"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.score(X_val, y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's a significant improvement, and it's much better than each of the individual classifiers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Once you have found one, try it on the test set. How much better does it perform compared to the individual classifiers?_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9689"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9437, 0.9474, 0.9603]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[estimator.score(X_test, y_test) for estimator in voting_clf.estimators_]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The voting classifier reduced the error rate from about 4.0% for our best model (the `MLPClassifier`) to just 3.1%. That's about 22.5% less errors, not bad!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Stacking Ensemble"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise: _Run the individual classifiers from the previous exercise to make predictions on the validation set, and create a new training set with the resulting predictions: each training instance is a vector containing the set of predictions from all your classifiers for an image, and the target is the image's class. Train a classifier on this new training set._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val_predictions = np.empty((len(X_val), len(estimators)), dtype=np.float32)\n",
    "\n",
    "for index, estimator in enumerate(estimators):\n",
    "    X_val_predictions[:, index] = estimator.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5., 5., 5., 5.],\n",
       "       [8., 8., 8., 8.],\n",
       "       [2., 2., 2., 2.],\n",
       "       ...,\n",
       "       [7., 7., 7., 7.],\n",
       "       [6., 6., 6., 6.],\n",
       "       [7., 7., 7., 7.]], dtype=float32)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_val_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=None,\n",
       "            oob_score=True, random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_forest_blender = RandomForestClassifier(n_estimators=200, oob_score=True, random_state=42)\n",
    "rnd_forest_blender.fit(X_val_predictions, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9624"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_forest_blender.oob_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You could fine-tune this blender or try other types of blenders (e.g., an `MLPClassifier`), then select the best one using cross-validation, as always."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise: _Congratulations, you have just trained a blender, and together with the classifiers they form a stacking ensemble! Now let's evaluate the ensemble on the test set. For each image in the test set, make predictions with all your classifiers, then feed the predictions to the blender to get the ensemble's predictions. How does it compare to the voting classifier you trained earlier?_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_predictions = np.empty((len(X_test), len(estimators)), dtype=np.float32)\n",
    "\n",
    "for index, estimator in enumerate(estimators):\n",
    "    X_test_predictions[:, index] = estimator.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = rnd_forest_blender.predict(X_test_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9601"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This stacking ensemble does not perform as well as the soft voting classifier we trained earlier, it's just as good as the best individual classifier."
   ]
  }
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